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hyperactivity disorder predicts internet addiction: Evidence from a longitudinal study
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Motivational but not executive dysfunction in Attention Deficit/Hyperactivity Disorder predicts Internet addiction: Evidence from a longitudinal study Bingping Zhou Conceptualization;Data curation;Writing-Original draft preparation;Visualization , Wei Zhang Conceptualization;Methodology;Software , Yaojin Li Investigation;Writing- Reviewing and Editing , Jinfeng Xue Supervision;Investigation;Software , Yanli Zhang-James Writing- Reviewing and Editing PII: DOI: Reference:
S0165-1781(19)31638-5 https://doi.org/10.1016/j.psychres.2020.112814 PSY 112814
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Psychiatry Research
Received date: Revised date: Accepted date:
30 July 2019 21 January 2020 25 January 2020
Please cite this article as: Bingping Zhou Conceptualization;Data curation;Writing-Original draft preparation;Visualiz Wei Zhang Conceptualization;Methodology;Software , Yaojin Li Investigation;Writing- Reviewing and Editing , Jinfeng Xue Supervision;Investigation;Software , Yanli Zhang-James Writing- Reviewing and Editing , Motivational but not executive dysfunction in Attention Deficit/Hyperactivity Disorder predicts Internet addiction: Evidence from a longitudinal study, Psychiatry Research (2020), doi: https://doi.org/10.1016/j.psychres.2020.112814
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Highlights
The causal priority between ADHD and Internet addiction among young adults was tested across a six-month interval. ADHD symptoms predicted Internet addiction severity but not vice versa. Three subtypes of dysfunction in ADHD were distinguished by cognitive tasks. Motivational dysfunction in ADHD is a better predictor of Internet addiction than executive dysfunction.
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Motivational but not executive dysfunction in Attention Deficit/Hyperactivity Disorder predicts Internet addiction: Evidence from a longitudinal study Bingping Zhoua,b,c, Wei Zhanga,b,c,, Yaojin Lia,b,c, Jinfeng Xued, Yanli Zhang-Jamese a
School of Psychology, Central China Normal University, Hubei, China Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China c Hubei Human Development and Mental Health Key Laboratory (Central China Normal University) d Medical School, Hunan University of Chinese Medicine, Hunan, China e State University of New York (SUNY) Upstate Medical University, Syracuse, NY, USA b
Corresponding author at: School of Psychology, Central China Normal University, No. 152 Luoyu Street, HongShan District, Wuhan, 430079, Hubei, China. E-mail: [email protected] (Wei Zhang) Phone number: (+86) 1552703555 2
Abstract This study tested the causal link between Attention Deficit/Hyperactivity Disorder (ADHD) and Internet addiction (IA) and investigated motivational and executive dysfunction as explanatory mechanisms in this association. A sample of 682 young adults completed self-report measures both at Time1 and Time2, six-months apart, including 54 ADHD participants diagnosed by the Conners‟ Adult ADHD Rating Scale and the Continuous Performance Test. According to the performance in four cognitive tasks, ADHD participants were classified into three groups based on the dual pathway model of ADHD: executive dysfunction (ED), motivational dysfunction (MD) and combined dysfunction (CD). Participants‟ severity of IA symptoms was assessed using the self-report Chen IA Scale. Results indicated that ADHD scores at Time1 predicted IA scores at Time2 but not vice versa. ADHD participants were easier to be IA than controls, while the severity of IA among the three ADHD groups changed differently. The MD and CD groups became more excessively engaged in Internet use over the course of the six-months while the ED group was unchanged. These findings identify ADHD as a potential risk factor for IA and suggest that motivational dysfunction, characterized by an excessive preference for immediate reward over delayed rewards, is a better predictor of IA than executive dysfunction.
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1. Introduction As the saying goes, “It never rains but it pours.” Internet addiction (IA), defined as the inability to control Internet use, which eventually causes marked impairments in psychological and social functions (Young, 1998, 2004, 2017), has been suggested to be associated with a variety of psychiatric disorders (Ho et al., 2014; Ko et al, 2012). Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common mental disorders associated with IA. Using an elementary school student sample, Yoo and colleagues first reported a strong association between IA and ADHD (Yoo et al., 2004). After then, numerous studies have demonstrated that people with ADHD symptoms, including the inattentive and hyperactive-impulsive subtypes, were at a high risk for IA compared to those without ADHD symptoms (see a recent meta-analysis, Wang et al., 2017). Although there is a lack of sufficient studies to clarify the direction of the causal relationship between these two disorders, most researchers suggested that ADHD was the predisposing factor of IA, because the onset of ADHD (at age 7 based on the clinical confirmation) generally predates the incipience of problematic internet use (Ho et al., 2014). Indeed, two prior prospective studies have indicated that ADHD symptoms could significantly predict the occurrence of IA in young adolescents (Chen et al., 2015; Ko et al., 2009). Yet there are still limited longitudinal studies supporting a causality between ADHD and IA (Wang et al., 2017), and lack of studies in older populations. Considering the fact that 2% to 7% of adults meet the criteria for ADHD (Pettersson et al., 2018; Simon et al., 2009) and the high prevalence of IA 4
among young adults (Chi et al., 2016; Lin et al., 2011), it is necessary to conduct follow-up studies to further test the causal relationship between this two disorders among young adults. This is the first objective of the current study. Furthermore, the mechanisms underlying the comorbidity of ADHD and IA are not clear. Some researchers focused on cognitive dysfunction observed in people with ADHD that meet the features of IA. For instance, Ko et al. (2012) proposed that impaired inhibition in individuals with ADHD (Rubia et al., 2005) might result in the difficulties in regulating Internet activities effectively and lead to problematic internet use. Moreover, individuals with ADHD are easily fretful and often seek immediate rewards (Castellanos and Tannock, 2002; Sonuga-Barke, 2005). Internet activities can provide prompt feedback and immediate reinforcement which relieve the feeling of boredom for individuals with ADHD (Ko et al., 2012). Interestingly, the possible explanations proposed by Ko et al. (2012) fit well with the dual pathway model of ADHD (Sonuga-Barke, 2002, 2003, 2005), although the authors did not explicitly state that. To explain the neuropsychological heterogeneity in ADHD, the dual pathway model states two distinct pathways contributing separately to ADHD. The first pathway involves executive dysfunction. Executive functions are higher-order, top-down, cognitive processes that allow appropriate set maintenance and shift, and facilitate the flexible pursuit of future goals (Barkley and Murphy, 2010; Craig et al., 2016; Silverstein et al., 2018; Sonuga-Barke, 2005). Executive functions contain abilities that include selection for information to manipulate, holding task-relevant information accessibly over time, inhibiting a 5
verbal or motor response, solving problems, and self-monitoring. The second pathway is the dysfunction in motivational processes. Motivational processes include the delay aversion, sensitivity to rewards, response costs and risky choices, and are independent of disinhibition problems. Motivational dysfunction in ADHD adults is associated with the disruption of the dopamine reward pathway, characterized by an excessive preference for immediate reward over delayed rewards (Sonuga-Barke, 2002, 2003, 2005; Toplak et al., 2005). Therefore, executive dysfunction may lead to the lack the necessary inhibitory control of Internet use, while motivational dysfunction would promote the seeking of novel stimuli and instant rewards from the virtual world. Using a wide range of tasks, such as response disinhibition, working memory impairment, delay aversion and risk taking, many studies have shown that executive dysfunction and motivational dysfunction, which characterize ADHD, are also apparent in IA (Cerniglia et al., 2017; Choi et al., 2014; Dong et al., 2012; Dong et al., 2010; Nie et al., 2016; Saville et al., 2010). Therefore, it is conceivable that one or both dysfunctions contribute to the comorbidity of ADHD and AI. Unfortunately, empirical evidence, especially from longitudinal research, is very scarce. Moreover, it is also important to examine the predictive validities of these two dysfunctions in relation to IA, which could not be answered in previous cross-sectional studies. Taken collectively, in the current study, we conducted a short-term longitudinal survey among college students in mainland China to answer two crucial research questions about the relationship between ADHD and IA. First, whether ADHD symptoms could significantly predict IA severity in a large cohort of young adults, as 6
previously reported in adolescents? Considering the potential reciprocal effect over a long period, that is, ADHD and IA may interact with each other as prior researchers have suggested (Wang et al., 2017), we conducted this research at two time points over a six-month interval. We expected that ADHD symptoms would predict IA severity in such a relatively short period and not the other way around. Second, we intent to examine the mechanistic relationship of ADHD and AI, specifically, whether executive or motivational dysfunction, or both in ADHD predicts IA? To address this issue, we adopted two most commonly administered executive tasks (Stop-signal Task to measure response inhibition and N-back task to measure working memory) and two tasks as measures of motivational style (Delay Discounting Task to measure delay aversion and Balloon Analogue Risk Task to measure risk taking) at Time 1 to distinguish three subtypes of ADHD: executive dysfunction, motivational dysfunction and combined dysfunction types, and then compared the changes in the severity of IA among these three subtypes.
2. Methods 2.1. Participants The initial sample was comprised of 1109 students between 18 and 22 years of age from two colleges in Wuhan, a city with the largest number of college students in China. The final sample was comprised of 682 college students (448 females, 234 males) who completed the tasks both at Time1 (T1) and Time2 (T2), and included 54 participants diagnosed with ADHD (36 females, 18 males) by Conners‟ Adult ADHD Rating Scale and the Continuous Performance Test. Comparisons of dropouts with 7
remaining participants at T2 showed no differences in their ADHD symptoms and IA severity at T1 (see Table 1). Attrition was mainly attributed to administrative reasons, including the job changes of two school counsellors who was involved in student recruitment and monitoring, as well as interruptions resulted from students‟ social affairs or internships. Four cognitive tasks at T1 were used to assign the ADHD participants into three subgroups (executive dysfunction, motivational dysfunction and combined dysfunction groups). Four ADHD participants who did not meet the group criteria were excluded.
17 participants with ADHD (13 females, 4 males)
were assigned to executive dysfunction group, 10 (6 females, 4 males) were assigned to motivational dysfunction group and 23 (16 females, 7 males) were assigned to combined dysfunction group. Additionally, 26 matched normal controls (NC) were randomly selected for comparison. See Fig.1 for a CONSORT diagram depicting the flow of the subjects and control. All subjects were administered the Chen Internet Addiction Scale (CIAS) to assess the severity of IA at both time points. In addition, the Structured Clinical Interview for DSM-IV (SCID) was performed to clarify mental disorders. Normal controls reported no history of any psychiatric disorder, and all participants were drug naïve. [Table 1] [Fig. 1]
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2.2. Measures 2.2.1. Conners’ Adult ADHD Rating Scale The Conners‟ Adult ADHD Rating Scale-Self-Report: Short Version (CAARS) (Conners et al., 1999) was used as a dimensional measure of current ADHD symptoms in a form suitable for adults, with 26 items rating from 0 („not at all, never‟) to 3 („very much, very frequently‟). In this study, we used a Chinese version that was revised by Wu et al. (2009). The overall ADHD Index score threshold of 39 (at least1.5 SD above population mean) was used to identify participants with empirically elevated symptom severity. The two coefficient alphas for the CAARS in this study were 0.88 at T1 and 0.91 at T2. 2.2.2. Continuous Performance Test The Continuous Performance Test (CPT)is a laboratory-based measure designed to assess the participants‟ sustained attention and impulsivity, which has shown one of the largest test effect sizes in comparisons of adults with ADHD and normal adults (Pettersson et al., 2018). We used the CPT as the complement of the self-reported method to ensure that all the ADHD participants diagnosed by CAARS have critical symptoms at T1. The CPT did not change the screening results of CAARS. 2.2.3. Chen Internet Addiction Scale The Chen Internet Addiction Scale (CIAS) is 26-item, 4-point Likert-type scale that has been widely used as the criteria for IA in Mainland China and Taiwan (Chen et al., 2003; Ko et al., 2014; Nie et al., 2016). The higher total score indicates a more
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serious IA problem. The two coefficient alphas for the CIAS in this study were 0.94 at T1 and 0.91 at T2. 2.2.4. Stop-Signal Task The stop signal task (SST) is a well validated task assessing the ability to inhibit dominant responses (Logan et al., 1997). In this study, SST was taken from Cambridge Neuropsychological Test Automated Battery (CANTAB), and the stop signal reaction time (SSRT) was adapted as the measure of inhibitory control. 2.2.5. N-Back Task The letter variant version of the n-back task is used widely as a measure of Working Memory (WM). The n-back task in this study employed three levels of complexity: 1-2-3 back tasks, which required participants to maintain and update the letters that were presented on the screen in their working memory. The currently presented letter was the “target” stimulus when it matched the one that presented previously. In the 1-back level, the target letter was the one that had appeared before. In the 2-back level, the target letter was the one that had appeared two letters before. In the 3-back level, the target letter was the one that had appeared three letters before. Participants were instructed to press the “A” key on the keyboard as soon as the target letter appeared and withhold any response when a non-target letter was presented. Following nine practice trials, participants were tested on the 1-, 2-and 3-back levels in that order, with 15 trials per level for a total of 45 trials. Every trial started with a presentation of a 500ms fixation point, followed by a letter displayed for 500ms. The interval between letters was 2000ms. The dependent variable was the response time 10
(RT) of correct judgment in this task. 2.2.6. Delay Discounting Task The Delay Discounting Task (DDT) was adapted from a published experimental study (Scheres et al., 2010). Five practice trials were presented before the final test. In this task, participants were instructed to make repeated choices between a small variable reward (2, 4, 6, or 8 cents) and a large constant reward (10 cents). The small variable rewards were delivered immediately (0 seconds) and the large constant rewards were delivered after a variable delay (5, 10, 20, 30, or 60 seconds). Each small immediate reward was paired twice with a delay for the large reward for a total of 40 trials. Each trial began with two airplanes on the computer screen, each carrying a quantity of money. The higher the plane flew, the longer the participants needed to wait. Participants had to select one plane as their final choice. If participants chose the 10-cent reward, they had to wait the corresponding time. Before the task, participants were told that they would be paid the real monetary amount they won. With the formula V=A/ (1+kD), in which V is subjective value of the delayed reward, A is the delayed reward, and D is the delay in seconds. The k value determines the speed of discounting and a higher k indicates higher impulsivity. 2.2.7. Balloon Analogue Risk Task The Balloon Analogue Risk Task (BART) was adapted from a published experimental study (Lejuez et al., 2002). This task includes a suite of 30 balloon pumping trials that were computer-simulated. Participants were instructed to sequentially inflate a balloon that could either grow larger or explode. Increasing 11
balloon inflation was associated with greater reward (5 cents/pump), but a larger balloon was associated with an increased probability of explosion, meaning all the accumulated reward would disappear. The mean number of pumps per balloon which did not include the exploded balloons was used as the measure of risk taking and impulsivity. 2.3. Procedure The study was approved by the Research Ethics Committee in the School of Psychology at Central China Normal University. Participants provided written informed consent to participate. Participants were assured that their names and answers were anonymous. Participants were told that they could decide not to participate or stop participating at any time. All the students who completed the questionnaire could get a small gift, and those who participated in the cognitive tasks received a reward of ¥10-20 based on their performance at the end of data collection. Data were collected at two sessions 6 months apart: All participants completed the questionnaires at T1. Those who reached the diagnostic criteria of ADHD were invited to complete the CPT as a complement questionnaire within a week. The four cognitive tasks were then administered. At T2, all participants only completed the questionnaires. 2.4. Statistical analysis A cross-lagged panel structural model using Mplus 7.0 was adopted to evaluate the relative strength of the cross-lagged relationships between the scores of ADHD and IA measured in T1 and T2. 12
Scores of the four cognitive tasks were transformed to z-scores before analysis. The z-scores of SST and N-Back Task were added together as an overall score (named Z-executive) for executive function. Similarly, the z-scores of DDT and BART were added together to generate Z-motivation as the indicator of a participant‟s motivational style. The higher overall z-score indicated the more severe dysfunction, and those who scored below the lower 27th percentile (Z-executive = -0.5; Z-motivation = -0.52) were considered to be cognitively well-functioned. Accordingly, those ADHD participants whose Z-executive was ≥ -0.5 while Z-motivation was < -0.5 were assigned to executive dysfunction group. Those whose Z-motivation was ≥ -0.5 while Z-executive was < -0.5 were assigned to motivational dysfunction group. And those whose Z-executive was ≥ -0.5 while Z-motivation was ≥ -0.5 were assigned to combined dysfunction group. Table 2 shows the grouping criteria. Differences of performance on the cognitive tasks among four groups (three ADHD groups and normal control (NC)) were compared using one-way ANOVA to verify the effectiveness of the group assignment. Finally, a repeated-measures ANOVA was performed to determine if there were significant changes in CIAS scores by group (three ADHD groups and NC) over time. [Table 2]
3. Results 3.1. Causal relationship between ADHD and Internet addiction As shown in Fig.2, cross-lagged regression test results revealed several significant relations. First, the severity of ADHD symptoms was associated positively 13
with the severity of IA at both time points. Second, after controlling for stability effects (i.e., the influence of a measure at T1 on the same measure at T2), the cross-lagged effect of IA at T1 on ADHD at T2 faded (β = 0.04, p > 0.05) , but the significant effect from ADHD at T1 on IA at T2 remained (β = 0.07, p < 0.05). This suggests that ADHD can predict IA in six months but not vice versa. [Fig. 2] 3.2. Performance of four groups on cognitive tasks Performance on four cognitive tasks among the three ADHD and one NC groups are shown in Table 3. The ANOVA results revealed that the ED and CD groups performed worse than the MD and NC groups regarding the executive tasks (SST and N-back). For the motivational tasks (DDT and BART), the MD and CD groups performed worse than the ED and NC groups. The significant differences observed between groups in the cognitive tasks have verified the effectiveness of the grouping method. [Table 3] 3.3. Differences between two times of IA test in four groups Table 3 and Fig. 3 present the severity differences of IA among the four groups at T1 and T2. The 2 (time) * 2 (group) repeated-measures ANOVA revealed a significant main effect of group, F (3, 72) = 8.41, p < 0.01, η2p = 0.26, that all three ADHD groups scored higher than the NC group on the CIAS (ps < 0.01). This further supports that ADHD symptoms can increase the risk of IA. Furthermore, a significant group-by-time interaction effect was obtained, F (3, 72) = 4.48, p < 0.05, η2p = 0.16. 14
Simple effect analysis revealed that the CIAS scores at T2 were significantly improved in both the MD group, F (1, 72) = 4.08, p < 0.05, and CD group, F (1, 72) = 4.83, p < 0.05. No significant changes were found in the ED group, F (1, 72) = 1.70, p = 0.20, and NC group, F (1, 72) = 2.94, p = 0.09. Thus, the results suggest that motivational but not executive dysfunction in ADHD predicts IA. [Fig. 3]
4. Discussion To our knowledge, this is the first study of a cross-lagged panel survey to establish the causal order in the empirical relationship between ADHD and IA in a large cohort of young adults. Our results provide a deep insight to the type of dysfunction contributing to the development of IA among people with ADHD. Consistent with previous studies, our results showed that ADHD symptoms were significantly associated with IA among young adults. Furthermore, ADHD symptoms were found to predict the severity of IA in the six-month follow-up but not vice versa. In a recent meta-analysis about IA and psychiatric co-morbidity (Ho et al., 2014), the authors estimated that the prevalence of ADHD among IA patients was 21.7% and suggested ADHD as a predisposing factor of IA because the onset of ADHD predates the incipience of IA. Ho and colleagues also suggested a reciprocal effect between IA and ADHD, in which the Internet usage might improve ADHD symptoms, although it is unlikely that IA could cause ADHD. In our current study, however, the short time span between two waves of surveys may be insufficient to aggravate or improve ADHD symptoms, since ADHD is a relatively stable and persistent disorder 15
(Biederman et al., 2007; Semeijn et al., 2016). By contrast, changes in severity of IA could be detected in just a few months (Calvete et al., 2017; Chen et al., 2015; Gámez-Guadix et al., 2015; Li et al., 2019). Our study made a notable contribution to clarification of the mechanisms underlying IA comorbidity with ADHD. We examined both executive and motivational dysfunctions in ADHD using four commonly administered cognitive tasks (Sonuga-Barke, 2002, 2005). The results that three groups of ADHDs with different type of dysfunctions were identified have illustrated the effectiveness of the method. Self-reported results at both time periods showed that all ADHD participants tended to be more addicted to the Internet than normal controls, while the severity of IA among the three groups of ADHD participants changed differently. The motivational and combined dysfunction groups of ADHD participants became more excessively engaged in the Internet after six months while the executive dysfunction group maintained unchanged, suggesting that motivational dysfunction in ADHD could be a better predictor of IA than executive dysfunction. As outlined earlier, cross sectional neuropsychological studies have indicated that the executive functions including response inhibition (Cao et al., 2007; Choi et al., 2014; Dong et al., 2012; Dong et al., 2011; Zhou et al., 2010; Zhou et al., 2014) and working memory (Zhou et al., 2015; Zhou et al., 2014) were related to symptoms of IA, and suggested that these executive dysfunctions, particularly response inhibition, were major contributors to the development and continued addictive use of the Internet (Brand et al., 2014). However, our study did not find a significant change in 16
the severity of IA among the executive dysfunction subgroup in six months. This indicated that IA might have the causal priority in its empirical relationship with executive dysfunction, suggesting that excessive Internet use could lead to damage in the prefrontal cortex and increased risk for executive dysfunction over time, instead of the opposite. Although this corollary is to be validated by more longitudinal studies in the future, our study has proved that executive dysfunction in ADHD could only maintain but not increase the severity of IA, at least across a short period. There might be other reasons to account for the higher CIAS scores in the executive dysfunction group of ADHDs than in normal controls. For example, impairments in social domain such as difficulties in interpersonal relationships (Bunford et al., 2014; Hoza et al., 2005) could render ADHD individuals to prefer the online world where these problems may be masked. Our study confirmed the causal role of motivational dysfunction in IA in subjects with ADHD. Unlike executive function, motivational processes are strongly associated with the brain‟s reward system (Buckholtz et al., 2010) and often involve sensory, motor, cognitive and emotional functions that work together (Salamone et al., 2015). In recent years, an increasing numbers of studies have focused on the impairment of reward system among Internet addicts (Ding et al., 2013; Hou et al., 2012; Jović and Đinđić, 2011). Many have suggested that enhanced reward sensitivity (Dong et al., 2011; Meerkerk et al., 2010) and delay aversion (Saville et al., 2011) were important features of IA. Therefore, Internet use by ADHD individuals with motivational dysfunction may be more easily rewarded by the positive outcomes it 17
yields (e.g. the pleasure effects) than individuals without ADHD, and Internet stimuli could in turn trigger more reward-seeking behaviors over term, creating a vicious circle that is difficult to break in the absence of appropriate intervention. Several limitations should be considered. First, it is difficult to accurately understand the development of the IA among ADHD individuals from two waves of data collection, and the six-month interval may be also too short to observe long-term changes in ADHD and IA symptoms. Longer-term follow-up studies are warranted and may reveal clearer patterns of changes and relationships that our current study missed. An example of this is the study carried by Gentile et al. (2011) in which pathological gaming symptoms among youths showed various trajectories in 3 years. Second, our findings may not be extrapolated to clinical patients. Our research sample was from the general population, and ADHD screening was based on structured rating scales rather than clinical diagnoses. The symptoms of clinical ADHD patients could be more severe than subjects in the current study. Further work using clinical samples is required to establish the reliability of our findings. Third, there was a sex distribution bias in the sample with an over-representation of females, which may result in lower rates of ADHD as well as lower rates of IA. An earlier cross-sectional study showed that the association between ADHD and IA was more significant in females than in males (Yen et al., 2009). Thus, although our results did not change when we included sex as a covariate in the analysis, the extensibility of the results still needs to be treated with caution. Forth, we used only four commonly used cognitive tasks in prior studies about ADHD and IA to test participants‟ dysfunctions. In future 18
studies, it might be helpful to administer more comprehensive neuropsychological testing to enhance the validity of the studies. Finally, this study might have overlooked the influence of some other factors, such as personality and family environment. The important roles of these factors in the explanation for an association between ADHD and IA have been demonstrated by other studies (Chou et al., 2015; Dalbudak and Evren, 2014). Despite of the above noted limitations, our study provides a new perspective in understanding the relationship between ADHD and IA. The findings indicate that ADHD symptoms could predict the severity of IA in a short time but not vice versa. Furthermore, we found that motivational dysfunction is a better predictor of IA than the executive dysfunction in ADHD. Thus, intervention that focus on helping those individuals with ADHD who demonstrate motivational dysfunction might substantially decrease the risk of IA.
Conflict of interest None to declare.
Acknowledgement This study was supported by the Fundamental Research Funds for the Chinese Central Universities (CCNU14A007) for Central China Normal University.
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Discriminative Validity of Neuropsychological Tests and Diagnostic Assessment Instruments. Journal of Attention Disorders. 22(11), 1019-1031. Rubia, K., Smith, A. B., Brammer, M. J., Toone, B., Taylor, E., 2005. Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD. Am. J. Psychiatry. 162(6), 1067-1075. Salamone, J. D., Koychev, I., Correa, M., McGuire, P., 2015. Neurobiological basis of motivational deficits in psychopathology. Eur. Neuropsychopharmacol. 25(8), 1225-1238. Saville, B. K., Gisbert, A., Kopp, J., Telesco, C., 2010. Internet addiction and delay discounting in college students. The Psychological Record. 60(2), 273-286. Saville, B. K., Gisbert, A., Kopp, J., Telesco, C., 2011. Internet addiction and delay discounting in college students. The Psychological Record. 60(2), 273-286. Scheres, A., Tontsch, C., Thoeny, A. L., Kaczkurkin, A., 2010. Temporal reward discounting in attention-deficit/hyperactivity disorder: the contribution of symptom domains, reward magnitude, and session length. Biol. Psychiatry. 67(7), 641-648. Semeijn, E. J., Comijs, H. C., de Vet, H. C., Kooij, J. J., Michielsen, M., Beekman, A. T., Deeg, D. J., 2016. Lifetime stability of ADHD symptoms in older adults. Atten Defic Hyperact Disord. 8(1), 13-20. Silverstein, M. J., Faraone, S. V., Leon, T. L., Biederman, J., Spencer, T. J., Adler, L. A., 2018. The Relationship Between Executive Function Deficits and DSM-5-Defined ADHD Symptoms. Journal of Attention Disorders. 24(1), 41-51. Simon, V., Czobor, P., Balint, S., Meszaros, A., Bitter, I., 2009. Prevalence and correlates of adult attention-deficit hyperactivity disorder: meta-analysis. Br J Psychiatry. 194(3), 204-211. Sonuga-Barke, E. J. S., 2002. Psychological heterogeneity in AD/HD—a dual pathway model of behaviour and cognition. Behav. Brain Res. 130(1-2), 29-36. Sonuga-Barke, E. J. S., 2003. The dual pathway model of AD/HD: an elaboration of neuro-developmental characteristics. Neurosci. Biobehav. Rev. 27(7), 593-604. Sonuga-Barke, E. J. S., 2005. Causal Models of Attention-Deficit/Hyperactivity Disorder: From Common Simple Deficits to Multiple Developmental Pathways. Biol. Psychiatry. 57(11), 1231-1238. Toplak, M. E., Jain, U., Tannock, R., 2005. Executive and motivational processes in adolescents with Attention-Deficit-Hyperactivity Disorder (ADHD). Behav. Brain Funct. 1, 8. Wang, B. Q., Yao, N. Q., Zhou, X., Liu, J., Lv, Z. T., 2017. The association between attention deficit/hyperactivity disorder and internet addiction: a systematic review and meta-analysis. BMC Psychiatry. 17(1), 260. Wu, X., Sun, J., Li, X., Guo, L., 2009. Reliability and Validity of Chinese Version of Conners' Adult ADHD Rating Scales Self-Report. Chinese Mental Health Journal. 5, 349-352. Yen, J.-Y., Yen, C.-F., Chen, C.-S., Tang, T.-C., Ko, C.-H., 2009. The Association between Adult ADHD Symptoms and Internet Addiction among College Students: The Gender Difference. CyberPsychol. Behav. 12(2), 187-191. Yoo, H. J., Cho, S. C., Ha, J., Yune, S. K., Kim, S. J., Hwang, J., et al., 2004. Attention deficit hyperactivity symptoms and internet addiction. Psychiatry Clin. Neurosci. 58(5), 487-494. Young, K. S., 1998. Internet addiction: The emergence of a new clinical disorder. CyberPsychol. Behav. 1(3), 237-244. Young, K. S., 2004. Internet addiction: A new clinical phenomenon and its consequences. Am. Behav. Sci. 48(4), 402-415. Young, K. S., 2017. The evolution of Internet addiction. Addict. Behav. 64, 229-230. 22
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disorders:
a
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with
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gambling.
Acta
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Table 1 Demographic profile and descriptive statistics among participants who completed both times and those who failed to complete Time 2.
age
Participated at both Time1
Participated only at
and Time2 (n = 682)
Time1 (n = 427)
19.69 ± 0.88
19.40 ± 0.95
Comparison
t = 5.27, p = 0.00 χ2 = 3.20, p = 0.07
gender Male
234 (39.8%)
170 (34.3%)
Female
448 (60.2%)
257 (65.7%)
Time1 CAARS (ADHD)
27.13 ± 9.48
26.17 ± 9.41
t = 1.65, p = 0.10
Time1 CIAS (IA)
52.74 ± 11.27
52.65 ± 11.24
t = 0.13, p = 0.89
CAARS = Conners‟ Adult ADHD Rating Scale, CIAS = Chen Internet Addiction Scale, IA = Internet Addiction.
Table 2 The method for grouping ADHD participants with different type defects Executive dysfunction
Motivational dysfunction group
Combined dysfunction group
group (ED)
(MD)
(CD)
Z-executive
≥ -0.5
< -0.5
≥ -0.5
Z-motivation
< -0.5
≥ -0.5
≥ -0.5
Z-executive = overall z-score of SSRT and N-back, Z-motivation = overall z-score of DDT and BART.
Table 3 Performance on cognitive tasks and scores of CIAS at Time1 and Time2 among groups ED group
MD group
CD group
NC group
F
Mean
SD
Mean
SD
Mean
SD
Mean
SD
269.97
38.19
228.04
44.65
273.55
60.65
214.96
69.69
5.46**
578.02
64.74
460.62
52.83
629.62
123.36
546.43
89.32
8.29**
0.04
0.02
0.06
0.04
0.06
0.03
0.04
0.02
4.43**
19.37
6.67
36.53
7.88
37.02
10.49
31.43
12.19
11.16**
Z-executive
0.43
0.51
-1.17
0.58
0.96
1.14
-0.53
1.33
13.56**
Z-motivation
-1.16
0.51
0.62
1.04
0.82
0.99
-0.20
0.89
18.67**
Time1
57.18
8.96
57.00
11.02
56.35
9.79
49.15
8.95
3.69*
Time2
54.35
11.87
62.70
7.48
60.43
10.19
46.15
9.06
11.14**
SST SSRT N-Back RT DDT k BART Pump
CIAS (IA)
ED = executive dysfunction, MD = motivational dysfunction, CD = combined dysfunction, NC = normal control; SD = standard deviation; CIAS = Chen Internet Addiction Scale, IA = Internet Addiction; Z-executive = overall z-score of SSRT and N-back, Z-motivation = overall z-score of DDT and BART. 24
*p<0.05, **p< 0.01.
Fig. 1. CONSORT flow diagram. ED = executive dysfunction group, MD = motivational dysfunction group, CD = combined dysfunction group, NC = normal control.
Fig. 2. Standardized regression coefficients in a cross-lagged panel model testing the relationship between ADHD and Internet addiction. *p< 0.05, **p< 0.01.
25
Fig. 3. Scores of CIAS (Internet addiction) in different groups at Time1 and Time2. ED = executive dysfunction, MD = motivational dysfunction, CD = combined dysfunction, NC = normal control. Error bars represent standard errors. *p< 0.05.
26
Author Statement Wei Zhang: Conceptualization, Methodology, Software. Bingping Zhou: Conceptualization, Data curation, Writing-Original draft preparation, Visualization. Yaojin Li: Investigation, Writing- Reviewing and Editing. Jinfeng Xue: Supervision, Investigation, Software. Yanli Zhang-James: Writing- Reviewing and Editing.
27
Motivational but not executive dysfunction in Attention Deficit/Hyperactivity Disorder predicts Internet addiction: Evidence from a longitudinal study Bingping Zhou Conceptualization;Data curation;Writing-Original draft preparation;Visualization , Wei Zhang Conceptualization;Methodology;Software , Yaojin Li Investigation;Writing- Reviewing and Editing , Jinfeng Xue Supervision;Investigation;Software , Yanli Zhang-James Writing- Reviewing and Editing PII: DOI: Reference:
S0165-1781(19)31638-5 https://doi.org/10.1016/j.psychres.2020.112814 PSY 112814
To appear in:
Psychiatry Research
Received date: Revised date: Accepted date:
30 July 2019 21 January 2020 25 January 2020
Please cite this article as: Bingping Zhou Conceptualization;Data curation;Writing-Original draft preparation;Visualiz Wei Zhang Conceptualization;Methodology;Software , Yaojin Li Investigation;Writing- Reviewing and Editing , Jinfeng Xue Supervision;Investigation;Software , Yanli Zhang-James Writing- Reviewing and Editing , Motivational but not executive dysfunction in Attention Deficit/Hyperactivity Disorder predicts Internet addiction: Evidence from a longitudinal study, Psychiatry Research (2020), doi: https://doi.org/10.1016/j.psychres.2020.112814
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Highlights
The causal priority between ADHD and Internet addiction among young adults was tested across a six-month interval. ADHD symptoms predicted Internet addiction severity but not vice versa. Three subtypes of dysfunction in ADHD were distinguished by cognitive tasks. Motivational dysfunction in ADHD is a better predictor of Internet addiction than executive dysfunction.
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Motivational but not executive dysfunction in Attention Deficit/Hyperactivity Disorder predicts Internet addiction: Evidence from a longitudinal study Bingping Zhoua,b,c, Wei Zhanga,b,c,, Yaojin Lia,b,c, Jinfeng Xued, Yanli Zhang-Jamese a
School of Psychology, Central China Normal University, Hubei, China Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China c Hubei Human Development and Mental Health Key Laboratory (Central China Normal University) d Medical School, Hunan University of Chinese Medicine, Hunan, China e State University of New York (SUNY) Upstate Medical University, Syracuse, NY, USA b
Corresponding author at: School of Psychology, Central China Normal University, No. 152 Luoyu Street, HongShan District, Wuhan, 430079, Hubei, China. E-mail: [email protected] (Wei Zhang) Phone number: (+86) 1552703555 2
Abstract This study tested the causal link between Attention Deficit/Hyperactivity Disorder (ADHD) and Internet addiction (IA) and investigated motivational and executive dysfunction as explanatory mechanisms in this association. A sample of 682 young adults completed self-report measures both at Time1 and Time2, six-months apart, including 54 ADHD participants diagnosed by the Conners‟ Adult ADHD Rating Scale and the Continuous Performance Test. According to the performance in four cognitive tasks, ADHD participants were classified into three groups based on the dual pathway model of ADHD: executive dysfunction (ED), motivational dysfunction (MD) and combined dysfunction (CD). Participants‟ severity of IA symptoms was assessed using the self-report Chen IA Scale. Results indicated that ADHD scores at Time1 predicted IA scores at Time2 but not vice versa. ADHD participants were easier to be IA than controls, while the severity of IA among the three ADHD groups changed differently. The MD and CD groups became more excessively engaged in Internet use over the course of the six-months while the ED group was unchanged. These findings identify ADHD as a potential risk factor for IA and suggest that motivational dysfunction, characterized by an excessive preference for immediate reward over delayed rewards, is a better predictor of IA than executive dysfunction.
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1. Introduction As the saying goes, “It never rains but it pours.” Internet addiction (IA), defined as the inability to control Internet use, which eventually causes marked impairments in psychological and social functions (Young, 1998, 2004, 2017), has been suggested to be associated with a variety of psychiatric disorders (Ho et al., 2014; Ko et al, 2012). Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common mental disorders associated with IA. Using an elementary school student sample, Yoo and colleagues first reported a strong association between IA and ADHD (Yoo et al., 2004). After then, numerous studies have demonstrated that people with ADHD symptoms, including the inattentive and hyperactive-impulsive subtypes, were at a high risk for IA compared to those without ADHD symptoms (see a recent meta-analysis, Wang et al., 2017). Although there is a lack of sufficient studies to clarify the direction of the causal relationship between these two disorders, most researchers suggested that ADHD was the predisposing factor of IA, because the onset of ADHD (at age 7 based on the clinical confirmation) generally predates the incipience of problematic internet use (Ho et al., 2014). Indeed, two prior prospective studies have indicated that ADHD symptoms could significantly predict the occurrence of IA in young adolescents (Chen et al., 2015; Ko et al., 2009). Yet there are still limited longitudinal studies supporting a causality between ADHD and IA (Wang et al., 2017), and lack of studies in older populations. Considering the fact that 2% to 7% of adults meet the criteria for ADHD (Pettersson et al., 2018; Simon et al., 2009) and the high prevalence of IA 4
among young adults (Chi et al., 2016; Lin et al., 2011), it is necessary to conduct follow-up studies to further test the causal relationship between this two disorders among young adults. This is the first objective of the current study. Furthermore, the mechanisms underlying the comorbidity of ADHD and IA are not clear. Some researchers focused on cognitive dysfunction observed in people with ADHD that meet the features of IA. For instance, Ko et al. (2012) proposed that impaired inhibition in individuals with ADHD (Rubia et al., 2005) might result in the difficulties in regulating Internet activities effectively and lead to problematic internet use. Moreover, individuals with ADHD are easily fretful and often seek immediate rewards (Castellanos and Tannock, 2002; Sonuga-Barke, 2005). Internet activities can provide prompt feedback and immediate reinforcement which relieve the feeling of boredom for individuals with ADHD (Ko et al., 2012). Interestingly, the possible explanations proposed by Ko et al. (2012) fit well with the dual pathway model of ADHD (Sonuga-Barke, 2002, 2003, 2005), although the authors did not explicitly state that. To explain the neuropsychological heterogeneity in ADHD, the dual pathway model states two distinct pathways contributing separately to ADHD. The first pathway involves executive dysfunction. Executive functions are higher-order, top-down, cognitive processes that allow appropriate set maintenance and shift, and facilitate the flexible pursuit of future goals (Barkley and Murphy, 2010; Craig et al., 2016; Silverstein et al., 2018; Sonuga-Barke, 2005). Executive functions contain abilities that include selection for information to manipulate, holding task-relevant information accessibly over time, inhibiting a 5
verbal or motor response, solving problems, and self-monitoring. The second pathway is the dysfunction in motivational processes. Motivational processes include the delay aversion, sensitivity to rewards, response costs and risky choices, and are independent of disinhibition problems. Motivational dysfunction in ADHD adults is associated with the disruption of the dopamine reward pathway, characterized by an excessive preference for immediate reward over delayed rewards (Sonuga-Barke, 2002, 2003, 2005; Toplak et al., 2005). Therefore, executive dysfunction may lead to the lack the necessary inhibitory control of Internet use, while motivational dysfunction would promote the seeking of novel stimuli and instant rewards from the virtual world. Using a wide range of tasks, such as response disinhibition, working memory impairment, delay aversion and risk taking, many studies have shown that executive dysfunction and motivational dysfunction, which characterize ADHD, are also apparent in IA (Cerniglia et al., 2017; Choi et al., 2014; Dong et al., 2012; Dong et al., 2010; Nie et al., 2016; Saville et al., 2010). Therefore, it is conceivable that one or both dysfunctions contribute to the comorbidity of ADHD and AI. Unfortunately, empirical evidence, especially from longitudinal research, is very scarce. Moreover, it is also important to examine the predictive validities of these two dysfunctions in relation to IA, which could not be answered in previous cross-sectional studies. Taken collectively, in the current study, we conducted a short-term longitudinal survey among college students in mainland China to answer two crucial research questions about the relationship between ADHD and IA. First, whether ADHD symptoms could significantly predict IA severity in a large cohort of young adults, as 6
previously reported in adolescents? Considering the potential reciprocal effect over a long period, that is, ADHD and IA may interact with each other as prior researchers have suggested (Wang et al., 2017), we conducted this research at two time points over a six-month interval. We expected that ADHD symptoms would predict IA severity in such a relatively short period and not the other way around. Second, we intent to examine the mechanistic relationship of ADHD and AI, specifically, whether executive or motivational dysfunction, or both in ADHD predicts IA? To address this issue, we adopted two most commonly administered executive tasks (Stop-signal Task to measure response inhibition and N-back task to measure working memory) and two tasks as measures of motivational style (Delay Discounting Task to measure delay aversion and Balloon Analogue Risk Task to measure risk taking) at Time 1 to distinguish three subtypes of ADHD: executive dysfunction, motivational dysfunction and combined dysfunction types, and then compared the changes in the severity of IA among these three subtypes.
2. Methods 2.1. Participants The initial sample was comprised of 1109 students between 18 and 22 years of age from two colleges in Wuhan, a city with the largest number of college students in China. The final sample was comprised of 682 college students (448 females, 234 males) who completed the tasks both at Time1 (T1) and Time2 (T2), and included 54 participants diagnosed with ADHD (36 females, 18 males) by Conners‟ Adult ADHD Rating Scale and the Continuous Performance Test. Comparisons of dropouts with 7
remaining participants at T2 showed no differences in their ADHD symptoms and IA severity at T1 (see Table 1). Attrition was mainly attributed to administrative reasons, including the job changes of two school counsellors who was involved in student recruitment and monitoring, as well as interruptions resulted from students‟ social affairs or internships. Four cognitive tasks at T1 were used to assign the ADHD participants into three subgroups (executive dysfunction, motivational dysfunction and combined dysfunction groups). Four ADHD participants who did not meet the group criteria were excluded.
17 participants with ADHD (13 females, 4 males)
were assigned to executive dysfunction group, 10 (6 females, 4 males) were assigned to motivational dysfunction group and 23 (16 females, 7 males) were assigned to combined dysfunction group. Additionally, 26 matched normal controls (NC) were randomly selected for comparison. See Fig.1 for a CONSORT diagram depicting the flow of the subjects and control. All subjects were administered the Chen Internet Addiction Scale (CIAS) to assess the severity of IA at both time points. In addition, the Structured Clinical Interview for DSM-IV (SCID) was performed to clarify mental disorders. Normal controls reported no history of any psychiatric disorder, and all participants were drug naïve. [Table 1] [Fig. 1]
8
2.2. Measures 2.2.1. Conners’ Adult ADHD Rating Scale The Conners‟ Adult ADHD Rating Scale-Self-Report: Short Version (CAARS) (Conners et al., 1999) was used as a dimensional measure of current ADHD symptoms in a form suitable for adults, with 26 items rating from 0 („not at all, never‟) to 3 („very much, very frequently‟). In this study, we used a Chinese version that was revised by Wu et al. (2009). The overall ADHD Index score threshold of 39 (at least1.5 SD above population mean) was used to identify participants with empirically elevated symptom severity. The two coefficient alphas for the CAARS in this study were 0.88 at T1 and 0.91 at T2. 2.2.2. Continuous Performance Test The Continuous Performance Test (CPT)is a laboratory-based measure designed to assess the participants‟ sustained attention and impulsivity, which has shown one of the largest test effect sizes in comparisons of adults with ADHD and normal adults (Pettersson et al., 2018). We used the CPT as the complement of the self-reported method to ensure that all the ADHD participants diagnosed by CAARS have critical symptoms at T1. The CPT did not change the screening results of CAARS. 2.2.3. Chen Internet Addiction Scale The Chen Internet Addiction Scale (CIAS) is 26-item, 4-point Likert-type scale that has been widely used as the criteria for IA in Mainland China and Taiwan (Chen et al., 2003; Ko et al., 2014; Nie et al., 2016). The higher total score indicates a more
9
serious IA problem. The two coefficient alphas for the CIAS in this study were 0.94 at T1 and 0.91 at T2. 2.2.4. Stop-Signal Task The stop signal task (SST) is a well validated task assessing the ability to inhibit dominant responses (Logan et al., 1997). In this study, SST was taken from Cambridge Neuropsychological Test Automated Battery (CANTAB), and the stop signal reaction time (SSRT) was adapted as the measure of inhibitory control. 2.2.5. N-Back Task The letter variant version of the n-back task is used widely as a measure of Working Memory (WM). The n-back task in this study employed three levels of complexity: 1-2-3 back tasks, which required participants to maintain and update the letters that were presented on the screen in their working memory. The currently presented letter was the “target” stimulus when it matched the one that presented previously. In the 1-back level, the target letter was the one that had appeared before. In the 2-back level, the target letter was the one that had appeared two letters before. In the 3-back level, the target letter was the one that had appeared three letters before. Participants were instructed to press the “A” key on the keyboard as soon as the target letter appeared and withhold any response when a non-target letter was presented. Following nine practice trials, participants were tested on the 1-, 2-and 3-back levels in that order, with 15 trials per level for a total of 45 trials. Every trial started with a presentation of a 500ms fixation point, followed by a letter displayed for 500ms. The interval between letters was 2000ms. The dependent variable was the response time 10
(RT) of correct judgment in this task. 2.2.6. Delay Discounting Task The Delay Discounting Task (DDT) was adapted from a published experimental study (Scheres et al., 2010). Five practice trials were presented before the final test. In this task, participants were instructed to make repeated choices between a small variable reward (2, 4, 6, or 8 cents) and a large constant reward (10 cents). The small variable rewards were delivered immediately (0 seconds) and the large constant rewards were delivered after a variable delay (5, 10, 20, 30, or 60 seconds). Each small immediate reward was paired twice with a delay for the large reward for a total of 40 trials. Each trial began with two airplanes on the computer screen, each carrying a quantity of money. The higher the plane flew, the longer the participants needed to wait. Participants had to select one plane as their final choice. If participants chose the 10-cent reward, they had to wait the corresponding time. Before the task, participants were told that they would be paid the real monetary amount they won. With the formula V=A/ (1+kD), in which V is subjective value of the delayed reward, A is the delayed reward, and D is the delay in seconds. The k value determines the speed of discounting and a higher k indicates higher impulsivity. 2.2.7. Balloon Analogue Risk Task The Balloon Analogue Risk Task (BART) was adapted from a published experimental study (Lejuez et al., 2002). This task includes a suite of 30 balloon pumping trials that were computer-simulated. Participants were instructed to sequentially inflate a balloon that could either grow larger or explode. Increasing 11
balloon inflation was associated with greater reward (5 cents/pump), but a larger balloon was associated with an increased probability of explosion, meaning all the accumulated reward would disappear. The mean number of pumps per balloon which did not include the exploded balloons was used as the measure of risk taking and impulsivity. 2.3. Procedure The study was approved by the Research Ethics Committee in the School of Psychology at Central China Normal University. Participants provided written informed consent to participate. Participants were assured that their names and answers were anonymous. Participants were told that they could decide not to participate or stop participating at any time. All the students who completed the questionnaire could get a small gift, and those who participated in the cognitive tasks received a reward of ¥10-20 based on their performance at the end of data collection. Data were collected at two sessions 6 months apart: All participants completed the questionnaires at T1. Those who reached the diagnostic criteria of ADHD were invited to complete the CPT as a complement questionnaire within a week. The four cognitive tasks were then administered. At T2, all participants only completed the questionnaires. 2.4. Statistical analysis A cross-lagged panel structural model using Mplus 7.0 was adopted to evaluate the relative strength of the cross-lagged relationships between the scores of ADHD and IA measured in T1 and T2. 12
Scores of the four cognitive tasks were transformed to z-scores before analysis. The z-scores of SST and N-Back Task were added together as an overall score (named Z-executive) for executive function. Similarly, the z-scores of DDT and BART were added together to generate Z-motivation as the indicator of a participant‟s motivational style. The higher overall z-score indicated the more severe dysfunction, and those who scored below the lower 27th percentile (Z-executive = -0.5; Z-motivation = -0.52) were considered to be cognitively well-functioned. Accordingly, those ADHD participants whose Z-executive was ≥ -0.5 while Z-motivation was < -0.5 were assigned to executive dysfunction group. Those whose Z-motivation was ≥ -0.5 while Z-executive was < -0.5 were assigned to motivational dysfunction group. And those whose Z-executive was ≥ -0.5 while Z-motivation was ≥ -0.5 were assigned to combined dysfunction group. Table 2 shows the grouping criteria. Differences of performance on the cognitive tasks among four groups (three ADHD groups and normal control (NC)) were compared using one-way ANOVA to verify the effectiveness of the group assignment. Finally, a repeated-measures ANOVA was performed to determine if there were significant changes in CIAS scores by group (three ADHD groups and NC) over time. [Table 2]
3. Results 3.1. Causal relationship between ADHD and Internet addiction As shown in Fig.2, cross-lagged regression test results revealed several significant relations. First, the severity of ADHD symptoms was associated positively 13
with the severity of IA at both time points. Second, after controlling for stability effects (i.e., the influence of a measure at T1 on the same measure at T2), the cross-lagged effect of IA at T1 on ADHD at T2 faded (β = 0.04, p > 0.05) , but the significant effect from ADHD at T1 on IA at T2 remained (β = 0.07, p < 0.05). This suggests that ADHD can predict IA in six months but not vice versa. [Fig. 2] 3.2. Performance of four groups on cognitive tasks Performance on four cognitive tasks among the three ADHD and one NC groups are shown in Table 3. The ANOVA results revealed that the ED and CD groups performed worse than the MD and NC groups regarding the executive tasks (SST and N-back). For the motivational tasks (DDT and BART), the MD and CD groups performed worse than the ED and NC groups. The significant differences observed between groups in the cognitive tasks have verified the effectiveness of the grouping method. [Table 3] 3.3. Differences between two times of IA test in four groups Table 3 and Fig. 3 present the severity differences of IA among the four groups at T1 and T2. The 2 (time) * 2 (group) repeated-measures ANOVA revealed a significant main effect of group, F (3, 72) = 8.41, p < 0.01, η2p = 0.26, that all three ADHD groups scored higher than the NC group on the CIAS (ps < 0.01). This further supports that ADHD symptoms can increase the risk of IA. Furthermore, a significant group-by-time interaction effect was obtained, F (3, 72) = 4.48, p < 0.05, η2p = 0.16. 14
Simple effect analysis revealed that the CIAS scores at T2 were significantly improved in both the MD group, F (1, 72) = 4.08, p < 0.05, and CD group, F (1, 72) = 4.83, p < 0.05. No significant changes were found in the ED group, F (1, 72) = 1.70, p = 0.20, and NC group, F (1, 72) = 2.94, p = 0.09. Thus, the results suggest that motivational but not executive dysfunction in ADHD predicts IA. [Fig. 3]
4. Discussion To our knowledge, this is the first study of a cross-lagged panel survey to establish the causal order in the empirical relationship between ADHD and IA in a large cohort of young adults. Our results provide a deep insight to the type of dysfunction contributing to the development of IA among people with ADHD. Consistent with previous studies, our results showed that ADHD symptoms were significantly associated with IA among young adults. Furthermore, ADHD symptoms were found to predict the severity of IA in the six-month follow-up but not vice versa. In a recent meta-analysis about IA and psychiatric co-morbidity (Ho et al., 2014), the authors estimated that the prevalence of ADHD among IA patients was 21.7% and suggested ADHD as a predisposing factor of IA because the onset of ADHD predates the incipience of IA. Ho and colleagues also suggested a reciprocal effect between IA and ADHD, in which the Internet usage might improve ADHD symptoms, although it is unlikely that IA could cause ADHD. In our current study, however, the short time span between two waves of surveys may be insufficient to aggravate or improve ADHD symptoms, since ADHD is a relatively stable and persistent disorder 15
(Biederman et al., 2007; Semeijn et al., 2016). By contrast, changes in severity of IA could be detected in just a few months (Calvete et al., 2017; Chen et al., 2015; Gámez-Guadix et al., 2015; Li et al., 2019). Our study made a notable contribution to clarification of the mechanisms underlying IA comorbidity with ADHD. We examined both executive and motivational dysfunctions in ADHD using four commonly administered cognitive tasks (Sonuga-Barke, 2002, 2005). The results that three groups of ADHDs with different type of dysfunctions were identified have illustrated the effectiveness of the method. Self-reported results at both time periods showed that all ADHD participants tended to be more addicted to the Internet than normal controls, while the severity of IA among the three groups of ADHD participants changed differently. The motivational and combined dysfunction groups of ADHD participants became more excessively engaged in the Internet after six months while the executive dysfunction group maintained unchanged, suggesting that motivational dysfunction in ADHD could be a better predictor of IA than executive dysfunction. As outlined earlier, cross sectional neuropsychological studies have indicated that the executive functions including response inhibition (Cao et al., 2007; Choi et al., 2014; Dong et al., 2012; Dong et al., 2011; Zhou et al., 2010; Zhou et al., 2014) and working memory (Zhou et al., 2015; Zhou et al., 2014) were related to symptoms of IA, and suggested that these executive dysfunctions, particularly response inhibition, were major contributors to the development and continued addictive use of the Internet (Brand et al., 2014). However, our study did not find a significant change in 16
the severity of IA among the executive dysfunction subgroup in six months. This indicated that IA might have the causal priority in its empirical relationship with executive dysfunction, suggesting that excessive Internet use could lead to damage in the prefrontal cortex and increased risk for executive dysfunction over time, instead of the opposite. Although this corollary is to be validated by more longitudinal studies in the future, our study has proved that executive dysfunction in ADHD could only maintain but not increase the severity of IA, at least across a short period. There might be other reasons to account for the higher CIAS scores in the executive dysfunction group of ADHDs than in normal controls. For example, impairments in social domain such as difficulties in interpersonal relationships (Bunford et al., 2014; Hoza et al., 2005) could render ADHD individuals to prefer the online world where these problems may be masked. Our study confirmed the causal role of motivational dysfunction in IA in subjects with ADHD. Unlike executive function, motivational processes are strongly associated with the brain‟s reward system (Buckholtz et al., 2010) and often involve sensory, motor, cognitive and emotional functions that work together (Salamone et al., 2015). In recent years, an increasing numbers of studies have focused on the impairment of reward system among Internet addicts (Ding et al., 2013; Hou et al., 2012; Jović and Đinđić, 2011). Many have suggested that enhanced reward sensitivity (Dong et al., 2011; Meerkerk et al., 2010) and delay aversion (Saville et al., 2011) were important features of IA. Therefore, Internet use by ADHD individuals with motivational dysfunction may be more easily rewarded by the positive outcomes it 17
yields (e.g. the pleasure effects) than individuals without ADHD, and Internet stimuli could in turn trigger more reward-seeking behaviors over term, creating a vicious circle that is difficult to break in the absence of appropriate intervention. Several limitations should be considered. First, it is difficult to accurately understand the development of the IA among ADHD individuals from two waves of data collection, and the six-month interval may be also too short to observe long-term changes in ADHD and IA symptoms. Longer-term follow-up studies are warranted and may reveal clearer patterns of changes and relationships that our current study missed. An example of this is the study carried by Gentile et al. (2011) in which pathological gaming symptoms among youths showed various trajectories in 3 years. Second, our findings may not be extrapolated to clinical patients. Our research sample was from the general population, and ADHD screening was based on structured rating scales rather than clinical diagnoses. The symptoms of clinical ADHD patients could be more severe than subjects in the current study. Further work using clinical samples is required to establish the reliability of our findings. Third, there was a sex distribution bias in the sample with an over-representation of females, which may result in lower rates of ADHD as well as lower rates of IA. An earlier cross-sectional study showed that the association between ADHD and IA was more significant in females than in males (Yen et al., 2009). Thus, although our results did not change when we included sex as a covariate in the analysis, the extensibility of the results still needs to be treated with caution. Forth, we used only four commonly used cognitive tasks in prior studies about ADHD and IA to test participants‟ dysfunctions. In future 18
studies, it might be helpful to administer more comprehensive neuropsychological testing to enhance the validity of the studies. Finally, this study might have overlooked the influence of some other factors, such as personality and family environment. The important roles of these factors in the explanation for an association between ADHD and IA have been demonstrated by other studies (Chou et al., 2015; Dalbudak and Evren, 2014). Despite of the above noted limitations, our study provides a new perspective in understanding the relationship between ADHD and IA. The findings indicate that ADHD symptoms could predict the severity of IA in a short time but not vice versa. Furthermore, we found that motivational dysfunction is a better predictor of IA than the executive dysfunction in ADHD. Thus, intervention that focus on helping those individuals with ADHD who demonstrate motivational dysfunction might substantially decrease the risk of IA.
Conflict of interest None to declare.
Acknowledgement This study was supported by the Fundamental Research Funds for the Chinese Central Universities (CCNU14A007) for Central China Normal University.
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disorders:
a
comparison
with
pathological
gambling.
Acta
Neuropsychiatrica. FirstView, 1-9. Zhou, Z., Zhu, H., Li, C., Wang, J., 2014. Internet Addictive Individuals Share Impulsivity and Executive Dysfunction with Alcohol-Dependent Patients. Front. Behav. Neurosci. 8.
23
Table 1 Demographic profile and descriptive statistics among participants who completed both times and those who failed to complete Time 2.
age
Participated at both Time1
Participated only at
and Time2 (n = 682)
Time1 (n = 427)
19.69 ± 0.88
19.40 ± 0.95
Comparison
t = 5.27, p = 0.00 χ2 = 3.20, p = 0.07
gender Male
234 (39.8%)
170 (34.3%)
Female
448 (60.2%)
257 (65.7%)
Time1 CAARS (ADHD)
27.13 ± 9.48
26.17 ± 9.41
t = 1.65, p = 0.10
Time1 CIAS (IA)
52.74 ± 11.27
52.65 ± 11.24
t = 0.13, p = 0.89
CAARS = Conners‟ Adult ADHD Rating Scale, CIAS = Chen Internet Addiction Scale, IA = Internet Addiction.
Table 2 The method for grouping ADHD participants with different type defects Executive dysfunction
Motivational dysfunction group
Combined dysfunction group
group (ED)
(MD)
(CD)
Z-executive
≥ -0.5
< -0.5
≥ -0.5
Z-motivation
< -0.5
≥ -0.5
≥ -0.5
Z-executive = overall z-score of SSRT and N-back, Z-motivation = overall z-score of DDT and BART.
Table 3 Performance on cognitive tasks and scores of CIAS at Time1 and Time2 among groups ED group
MD group
CD group
NC group
F
Mean
SD
Mean
SD
Mean
SD
Mean
SD
269.97
38.19
228.04
44.65
273.55
60.65
214.96
69.69
5.46**
578.02
64.74
460.62
52.83
629.62
123.36
546.43
89.32
8.29**
0.04
0.02
0.06
0.04
0.06
0.03
0.04
0.02
4.43**
19.37
6.67
36.53
7.88
37.02
10.49
31.43
12.19
11.16**
Z-executive
0.43
0.51
-1.17
0.58
0.96
1.14
-0.53
1.33
13.56**
Z-motivation
-1.16
0.51
0.62
1.04
0.82
0.99
-0.20
0.89
18.67**
Time1
57.18
8.96
57.00
11.02
56.35
9.79
49.15
8.95
3.69*
Time2
54.35
11.87
62.70
7.48
60.43
10.19
46.15
9.06
11.14**
SST SSRT N-Back RT DDT k BART Pump
CIAS (IA)
ED = executive dysfunction, MD = motivational dysfunction, CD = combined dysfunction, NC = normal control; SD = standard deviation; CIAS = Chen Internet Addiction Scale, IA = Internet Addiction; Z-executive = overall z-score of SSRT and N-back, Z-motivation = overall z-score of DDT and BART. 24
*p<0.05, **p< 0.01.
Fig. 1. CONSORT flow diagram. ED = executive dysfunction group, MD = motivational dysfunction group, CD = combined dysfunction group, NC = normal control.
Fig. 2. Standardized regression coefficients in a cross-lagged panel model testing the relationship between ADHD and Internet addiction. *p< 0.05, **p< 0.01.
25
Fig. 3. Scores of CIAS (Internet addiction) in different groups at Time1 and Time2. ED = executive dysfunction, MD = motivational dysfunction, CD = combined dysfunction, NC = normal control. Error bars represent standard errors. *p< 0.05.
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Author Statement Wei Zhang: Conceptualization, Methodology, Software. Bingping Zhou: Conceptualization, Data curation, Writing-Original draft preparation, Visualization. Yaojin Li: Investigation, Writing- Reviewing and Editing. Jinfeng Xue: Supervision, Investigation, Software. Yanli Zhang-James: Writing- Reviewing and Editing.
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