Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-22T18:36:20.574Z Has data issue: false hasContentIssue false
  • English
  • Français

Cognitive deficits in problematic internet use: meta-analysis of 40 studies

Published online by Cambridge University Press:  20 February 2019

Konstantinos Ioannidis Open the ORCID record for Konstantinos Ioannidis [Opens in a new window] ,
Roxanne Hook ,
Anna E. Goudriaan ,
Simon Vlies ,
Naomi A. Fineberg ,
Jon E. Grant  and
Samuel R. Chamberlain Open the ORCID record for Samuel R. Chamberlain [Opens in a new window]
Konstantinos Ioannidis*
Affiliation:
Consultant Psychiatrist, Cambridge and Peterborough NHS Foundation Trust; and Honorary Visiting Fellow, Department of Psychiatry, University of Cambridge, UK
Roxanne Hook
Affiliation:
Research Assistant, Department of Psychiatry, University of Cambridge, UK
Anna E. Goudriaan
Affiliation:
Professor in Addiction, Academic Medical Center, Department of Psychiatry and Amsterdam Institute for Addiction Research, University of Amsterdam; and Arkin Mental Health Care, Netherlands
Simon Vlies
Affiliation:
Foundation Doctor Year 1, Cambridge and Peterborough NHS Foundation Trust, UK
Naomi A. Fineberg
Affiliation:
Consultant Psychiatrist and Visiting Professor, Hertfordshire Partnership University NHS Foundation Trust, University of Hertfordshire; and Senior Clinical Research Fellow, University of Cambridge School of Clinical Medicine, UK
Jon E. Grant
Affiliation:
Professor, Department of Psychiatry, University of Chicago, Pritzker School of Medicine, USA
Samuel R. Chamberlain
Affiliation:
Department of Psychiatry, University of Cambridge; and Peterborough NHS Foundation Trust, Cambridge, UK
*
Correspondence: Konstantinos Ioannidis, S3 Eating Disorders, Addenbrookes Hospital, Hills Road, Cambridge, CB2 0QQ, UK. Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Background

Excessive use of the internet is increasingly recognised as a global public health concern. Individual studies have reported cognitive impairment in problematic internet use (PIU), but have suffered from various methodological limitations. Confirmation of cognitive deficits in PIU would support the neurobiological plausibility of this disorder.

Aims

To conduct a rigorous meta-analysis of cognitive performance in PIU from case–control studies; and to assess the impact of study quality, the main type of online behaviour (for example gaming) and other parameters on the findings.

Method

A systematic literature review was conducted of peer-reviewed case–controlled studies comparing cognition in people with PIU (broadly defined) with that of healthy controls. Findings were extracted and subjected to a meta-analysis where at least four publications existed for a given cognitive domain of interest.

Results

The meta-analysis comprised 2922 participants across 40 studies. Compared with controls, PIU was associated with significant impairment in inhibitory control (Stroop task Hedge's g = 0.53 (s.e. = 0.19–0.87), stop-signal task g = 0.42 (s.e. = 0.17–0.66), go/no-go task g = 0.51 (s.e. = 0.26–0.75)), decision-making (g = 0.49 (s.e. = 0.28–0.70)) and working memory (g = 0.40 (s.e. = 0.20–0.82)). Whether or not gaming was the predominant type of online behaviour did not significantly moderate the observed cognitive effects; nor did age, gender, geographical area of reporting or the presence of comorbidities.

Conclusions

PIU is associated with decrements across a range of neuropsychological domains, irrespective of geographical location, supporting its cross-cultural and biological validity. These findings also suggest a common neurobiological vulnerability across PIU behaviours, including gaming, rather than a dissimilar neurocognitive profile for internet gaming disorder.

Declaration of interest

S.R.C. consults for Cambridge Cognition and Shire. K.I.’s research activities were supported by Health Education East of England Higher Training Special interest sessions. A.E.G.'s research has been funded by Innovational grant (VIDI-scheme) from ZonMW: (91713354). N.A.F. has received research support from Lundbeck, Glaxo-SmithKline, European College of Neuropsychopharmacology (ECNP), Servier, Cephalon, Astra Zeneca, Medical Research Council (UK), National Institute for Health Research, Wellcome Foundation, University of Hertfordshire, EU (FP7) and Shire. N.A.F. has received honoraria for lectures at scientific meetings from Abbott, Otsuka, Lundbeck, Servier, Astra Zeneca, Jazz pharmaceuticals, Bristol Myers Squibb, UK College of Mental Health Pharmacists and British Association for Psychopharmacology (BAP). N.A.F. has received financial support to attend scientific meetings from RANZCP, Shire, Janssen, Lundbeck, Servier, Novartis, Bristol Myers Squibb, Cephalon, International College of Obsessive-Compulsive Spectrum Disorders, International Society for Behavioral Addiction, CINP, IFMAD, ECNP, BAP, the World Health Organization and the Royal College of Psychiatrists. N.A.F. has received financial royalties for publications from Oxford University Press and payment for editorial duties from Taylor and Francis. J.E.G. reports grants from the National Center for Responsible Gaming, Forest Pharmaceuticals, Takeda, Brainsway, and Roche and others from Oxford Press, Norton, McGraw-Hill and American Psychiatric Publishing outside of the submitted work.

Keywords

Behavioral addictioninternet addictioninternet gaming disorderproblematic internet usemeta-analysis
Type
Review article
Information
The British Journal of Psychiatry , Volume 215 , Issue 5 , November 2019 , pp. 639 - 646
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Royal College of Psychiatrists 2019

Introduction

Since its inception in the 1980s, the internet has become a global phenomenon.Reference Grohol and Gackenbach1–Reference Young3 Some adolescents and adults develop a problem controlling their use of the internet, leading to marked functional impairment (for example lower quality of life, worse scholastic outcomes and occupational difficulties).Reference Kuss, Griffiths, Karila and Billieux4 Historically, the term ‘internet addiction disorder’ started appearing in the mid-ninetiesReference Grohol and Gackenbach1–Reference Young3 to describe a maladaptive pattern of use of online resources that shared the characteristics of an addictive or compulsive disorder. Since then, the diagnostic criteria, assessment tools and conceptual formulation of internet addiction have been controversial.Reference Beard and Wolf5, Reference Kardefelt-Winther6 Theoretically different views on problematic use of the internet exist, as exemplified by the terms referred to, for example compulsive internet use, problematic internet use (PIU), internet addiction. DSM-5 features internet gaming disorder (IGD) in Section III, as a condition in need of further study, but does not include the more general disorder of PIU.7 DSM-5 highlights that IGD appears to be most common in male adolescents, aged 12–20 years.7

The concept of PIU was coined to avoid classification with addictions until more about the disorder was understood.Reference Shapira, Lessig, Goldsmith, Szabo, Lazoritz and Gold8, Reference Aboujaoude9 It has been noted that a broad range of excessive online behaviours are associated with marked functional impairment as well as with profound psychiatric sequalae, including in adolescents,Reference Pontes10 adultsReference Ioannidis, Chamberlain, Treder, Kiraly, Leppink and Redden11 and mixed samples of both.Reference Bianchini, Cecilia, Roncone and Cofini12 Based on empirical evidence, we define PIU as excessive online activities likely to be associated with marked functional impairment, including compulsive online buying, gambling, cybersex, as well as excessive use of online streaming and social media that have addictive, impulsive and/or compulsive elements.Reference Ioannidis, Chamberlain, Treder, Kiraly, Leppink and Redden11, Reference Király, Griffiths and Demetrovics13 Age may influence the presentation of PIU and its comorbidities. For example, one study found that attention-deficit hyperactivity disorder (ADHD) and social anxiety were associated with PIU in young adults; whereas generalised anxiety disorder and obsessive–compulsive disorder (OCD) were associated with PIU in older adults.Reference Ioannidis, Treder, Chamberlain, Kiraly, Redden and Stein14 Thus, PIU can occur in younger and older individuals but may present differently as a function of age. The debate is still ongoing as to whether PIU should be classified as an addictive, impulse controlReference Beard and Wolf5 or obsessive–compulsive related disorder.Reference Block15, Reference Zohar16

Neurobiology of problematic internet use

Understanding of the neurobiological underpinnings of a given mental disorder is vital for optimising disease models, classification and treatment approaches; as well as in understanding how it may relate to other disorders. In the case of excessive use of the internet, research in this area has the additional utility of helping to confirm or refute its validity. Currently, little is known about the neurocognitive determinants of PIU. Examining the cognitive performance of people with PIU to identify deficits (i.e. significantly worse performance compared against matched healthy controls) can provide insights into the neuropsychological mechanisms underpinning the disorder, and possible overlap with other psychiatric conditions. Conceptually, as noted above, PIU may share parallels with behavioural addiction, incorporating features such as escalating use over time, loss of control, concealing excessive use from others, failed attempts to cut back, and psychological distress when/if prevented from using the internet.Reference Young3, Reference Chamberlain, Lochner, Stein, Goudriaan, van Holst and Zohar17 In integrating research on PIU phenomena, the interaction of person-affect-cognition-execution model was developed by Brand and colleagues.Reference Brand, Young, Laier, Wölfling and Potenza18 Within this conceptual framework, reductions in executive functioning and inhibitory control contribute to engagement in online behaviours, leading to gratification and ultimately contributing to the emergence and persistence of PIU.

Despite growing numbers of published case–control studies examining cognition in this context, there is a paucity of rigorous meta-analyses from which to draw firm conclusions and examine potential moderators. In a meta-analysis restricted to IGD and one cognitive domain, a significant decrement was found for response inhibition compared with controls.Reference Argyriou, Davison and Lee19 Current models of PIU suggest that a broader range of cognitive failures may contribute including top–down inhibitory control, working memory and decision-making.Reference Chamberlain, Redden, Leppink and Grant20 The aim of the current study was to conduct a rigorous systematic review and meta-analysis of cognitive findings in PIU from case–control studies, including in adolescents and adults, reported in the peer-reviewed literature. We hypothesised, based on findings from individual studies and parallels between PIU and other related disorders, such as problematic gambling, that the condition would be associated with marked impairments across the above cognitive domains.

Method

Our meta-analysis protocol followed the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelinesReference Stroup, Berlin, Morton, Olkin, Williamson and Rennie21 and was preregistered electronically and published online on the PROSPERO International prospective register of systematic reviews (available from: http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42017080405).

Search strategy

Our search and screening strategy is outlined in Fig. 1. The search string was determined by consensus among the coauthors. The PubMed search was conducted with the following string: [“cognitive” OR “cognition” OR “memory” OR “executive” OR “attention” OR “decision-making” OR “gambling task” OR “inhibition” OR “stroop” OR “stop-signal” OR “go no go” OR “go/no-go” OR “gng” AND “internet use” OR “internet addiction” OR “gaming addiction” OR “PIU” OR “PUI” OR “internet gaming disorder”]. The initial search yielded 2908 results. The majority of these were excluded based on reading of the title and abstract, as a result of being out-of-scope (for example papers not measuring cognition, without a suitable control group or unrelated to PIU). This yielded 138 possibly eligible papers for inclusion. We then undertook a consensus meeting involving three members of the study team and examined full texts to exclude papers that were out-of-scope; references of full-text documents were also screened for further papers within scope.

SST, stop-signal task; STROOP, Stroop attentional inhibition task; Go/No-Go, go/no-go motor inhibitory control task. Please note that some studies in the final set examined more than one domain included in the final analysis.

Fig. 1 Search strategy followed for meta-analysis.

Inclusion criteria

We included all studies that (a) were published in scholarly peer-reviewed journals between 1995 and October 2017; (b) were written in English or provided an English translation; (c) examined a cognitive domain that was also measured in at least three other studies (i.e. sufficient n for valid meta-analysis); (d) examined cognitive measures of participants with PIU (used in its wider meaning to include the full spectrum of ‘addictive use of the internet’, ‘problematic internet use’ and ‘internet gaming disorder’) versus healthy controls and (e) included necessary information to calculate effect sizes. Where a given paper had not reported necessary information to calculate effect sizes the study team contacted the paper's authors via email to request this information.

Exclusion criteria

We excluded studies that (a) did not report cognitive measures; (b) used non-standard cognitive tasks (those tailored to a particular study where independent replication would not have been possible; and/or those not focusing on a recognised cognitive domain); (c) did not have a healthy comparison group; (d) lacked the required measures for meta-analysis (and such information was not provided within 4 weeks by the paper's authors); and (e) were published only in the grey literature (including conference papers, non-peer-reviewed publications, doctoral theses; as these sources are not necessarily subject to the same journal-level rigorous peer-review procedures as non-grey literature).

Data collection and analysis

Data were extracted from the original papers or were provided by the authors of each study. Information from the included studies was recorded in an electronic spreadsheet and different types of data were extracted from each study including: (a) a geographical determinant in which the data collection occurred; (b) key demographics of the participants (age as categorised by mean age reported in the sample: children 0–12, youth 12–24, adults 24–55, older people ≥55; gender distribution in the sample as ‘male only’, ‘female only’, or ‘mixed’); (c) operationalisation of PIU including instrument used and cut-off variant; (d) reported psychiatric comorbidities in the sample; (e) effects of PIU on cognitive measures; (f) quality scores. The quality assurance control was performed independently by two psychiatrists (K.I., S.R.C.; Cohen's kappa 0.96), who then met together to arrive at a consensus. All papers in scope were assessed against the quality standard individually and received a score between 0 and 10 (for quality scoring details see supplementary material ‘Quality assurance’ and Table 1 available at https://doi.org/10.1192/bjp.2019.3).

The full list and references of studies that entered the meta-analysis are reported in supplementary Table 2. Data were analysed using statistical software R version 3.4.2. Meta-analysis was performed using packages of ‘robumeta’ and ‘metafor’.Reference Quintana22 To provide a more generalisable model estimate, a random-effects model was used in all cases. The R code used for this analysis is shared in the supplement (see supplementary material ‘R code’), to support reproducible research. To compare PIU and control groups in terms of quantitative measures of cognitive performance we used mean scores and s.d. to calculate standardised mean difference measures, which were used to produce random-effects models for each different cognitive domain under investigation. Statistical significance was defined as P < 0.05 two-tailed throughout, and standard effect sizes were also reported. Moderator analysis was conducted to examine potential effects of the following on the results: age, gender (i.e. ‘males only’ versus ‘mixed’), presence of comorbidities (i.e. psychiatric comorbidities in the sample versus not), quality of study, whether or not online gaming was the predominant type of online activity (IGD versus PIU) and geographical area of reported study. Publication bias was assessed using regression tests for funnel plot asymmetryReference Egger, Davey Smith, Schneider and Minder23 and, where appropriate, the trim and fill method.Reference Duval and Tweedie24 Heterogeneity was quantified using tau-squared and Q-tests. For more information about the cognitive tests included in the meta-analysis see supplementary material ‘Description of cognitive domains and key outcome measures’.

Results

The number of data studies and total pooled sample sizes used in the meta-analysis are summarised in Table 1. Sufficient suitable data were found for meta-analysis of the following cognitive domains (tasks): motor inhibition (go/no-go), pre-potent motor inhibition (stop-signal), decision-making (Cambridge Gambling Task, Iowa Gambling Task, game of dice and Balloon Analogue Risk), working memory (digit span, spatial working memory) and discounting. The mean quality scores for the included studies, expressed as percentage of maximum, was: 68% (s.d. = 21%, range 2–9) (see supplementary Table 1 for full details). Effects of scores in moderation analysis are reported later. Most studies (approximately 80%) screened for affective disorders and substance misuse using validated instruments, whereas relatively few (<10%) screened for impulse-control disorders and gambling disorder. Another limitation of the extant data was that most studies were conducted in relatively young adults hence the association between PIU and cognition in older age groups could not be addressed.

Table 1 Total pooled sample sizes and model estimate measures for different cognitive domains

PIU, problematic internet use; GNG, go/no-go task; SST, stop-signal task. Some studies analysed more than one domain.

a. P-values here describe the probability of obtaining the observed model estimates under null hypothesis (no true differences between groups).

b. Adjusted model estimate after trim and fill method was applied because of publication bias.

c. Not further analysed because of publication bias and other methodological limitations (see supplementary material 'Discounting' and supplementary Tables 3 and 4.).

Figure 2(a) shows results from the meta-analysis of motor inhibitory control domains, where it can be seen that PIU was associated with significant impairment on go/no-go and stop-signal tasks versus controls with small-medium effect sizes (Hedge's g = 0.51 and Hedge's g = 0.42, respectively, see also Fig. 3). Figure 2(b) shows meta-analytic results for the domains of attentional inhibition (colour-word Stroop), decision-making and working memory. PIU was associated with significant impairment versus controls across all three domains with small-medium effect sizes (Hedge's g = 0.53, Hedge's g = 0.49 and Hedge's g = 0.51, respectively). The discounting domain was excluded and not considered further due to methodological limitations (see supplementary material ‘Discounting’ and supplementary Figures 3 and 4).

Forest plots for various cognitive domains of problematic internet use participants versus controls; effect sizes are Hedge's g; positive values indicate people with problematic internet use performed worse than controls. aEffect size for working memory domain here is reported uncorrected. RE, Random effects.

Fig. 2 Forest plots for (a) motor inhibitory control cognitive domains; and (b) for Stroop inhibitory control, decision-making and working memory cognitive domains.

(a) Attentional inhibition (Stroop task) test for plot asymmetry: z = 1.77, P = 0.078; (b) motor inhibitory control (go/no-go task) test for plot asymmetry: z = 0.46, P = 0.64; (c) motor inhibitory control (stop-signal task) test for plot asymmetry: z = 0.43, P = 0.66; (d) decision-making test for plot asymmetry: z = 1.1, P = 0.27; (e) discounting test for plot asymmetry: z = –2.7670, P = 0.0057; (f) working memory test for plot asymmetry: z = 0.88, P = 0.37. Meta-analysis funnels plots by cognitive domain; z- and P-values reported from regression test for funnel plot asymmetry (mixed-effects meta-regression model). Evidence of publication bias identified in the domains of discounting and working memory. The trim and fill method was used although effect size changed only for working memory (as indicated by the dotted line (non-corrected effect size 0.51)).

Fig. 3 Funnel plots by cognitive domain.

Evidence of publication bias was observed for the working memory domain, but the finding retained statistical significance when the trim and fill approach was used (see also Fig. 3). Homogeneity metrics are presented in full in supplementary Table 5. High heterogeneity was identified in Stroop studies and low to moderate heterogeneity was found for the other examined cognitive domains.

Age, gender, presence of comorbidities, whether or not gaming was the predominant online activity and geographical area were not significant moderating factors in any of the cognitive domains examined (all P > 0.05 non-corrected). In some cases, analysis was not possible because of lack of comparison groups. For example, Stroop and stop-signal studies had only been performed in youth (adolescents and young adults) and Stroop studies were only performed in populations lacking comorbidities. Quality of study was a significant moderating variable in stop-signal task (P = 0.032) with all higher quality studiesReference Chamberlain, Redden, Leppink and Grant20, Reference Chen, Huang, Yen, Chen, Liu and Yen25, Reference Ding, Sun, Sun, Chen, Zhou and Zhuang26 (quality mean  9/10) reporting smaller and non-statistically significant effects, and the two relatively lower quality studiesReference Dong, Lu, Zhou and Zhao27, Reference Kim, Lee, Choi, Kwak, Hwang and Kim28 (quality mean 7/10) reporting higher and statistically significant effects. Study quality was not a significant moderator for the other cognitive domains. More details on moderator analysis results are presented in the supplementary Table 6.

Discussion

Main findings

This is the first study to amass all available information from case–control studies of cognitive performance in people with PIU. We defined PIU as excessive online activities likely to be associated with marked functional impairment, including compulsive online buying, gambling, cybersex, as well as excessive use of online streaming and social media that have addictive, impulsive and/or compulsive elements. In meta-analysis, PIU was associated with significant cognitive deficits in attentional inhibition, motor inhibition (and pre-potent motor inhibition), decision-making and working memory, in line with our a priori hypothesis and supporting recent conceptualisations of PIU that implicate cognitive dysfunction in its pathophysiology.Reference Chamberlain, Lochner, Stein, Goudriaan, van Holst and Zohar17, Reference Brand, Young, Laier, Wölfling and Potenza18

These findings were not significantly moderated by whether or not online gaming was the predominant form of online behaviour, nor by geographical site, age, gender or comorbidities. Study quality did not significantly moderate the results, except for evidence of lesser stop-signal impairment for studies that were of higher quality. These neurocognitive results support the existence of underlying frontostriatal dysfunction in PIU, and highlight the need for international collaborations using standardised measures to further elucidate its precise neurobiological underpinnings and the specificity of deficits in given domains. These findings also suggest a common neurobiological vulnerability across PIU behaviours, including gaming.

Comparison with findings from other studies

Two previous systematic reviews examined ‘higher order’ meta-cognitive constructs that are relevant for IGD, including escapism, social identity and acceptance and beliefs about game reward,Reference King and Delfabbro60, Reference Pontes and Griffiths61 without providing a quantitative measure of cognition nor covering in detail neurocognitive performance. Therefore, in the wider context of existing literature, our study advances our knowledge of the neurocognitive aspects of PIU.

One previous meta-analysis of response inhibition was conducted in gaming disorder, which reported significant impairment.Reference Argyriou, Davison and Lee19 The current study extends beyond this prior meta-analysis by also considering the impact of study quality, and including a much larger range of available data. Problematic internet users are characterised by elevated behavioural impulsivity and compulsivity,Reference Ioannidis, Chamberlain, Treder, Kiraly, Leppink and Redden11, Reference Block15 which are characteristics of a wide range of psychiatric disorders, including ADHD, OCD, impulse control and substance use disorders.

Comorbidity

The majority of studies in this meta-analysis screened for mainstream mental disorders (such as affective disorders (78%) or substance misuse (80%)) using validated instruments. However, very few indeed used appropriate screening tools to identify comorbid impulse-control disorders (for example gambling disorder, ADHD) (7.5%). As such, the current meta-analysis cannot fully assess the contribution of comorbid impulsive disorders to the observed cognitive deficits. Data elsewhere suggest that cognitive problems are more pronounced in individuals with PIU with comorbid impulse-control disorders.Reference Chamberlain, Ioannidis and Grant62 Nonetheless, the results of this meta-analysis demonstrate that people with PIU have measurable deficits versus controls in cognitive performance, which may have implications for day-to-day functioning, even if they partly stem from unmeasured comorbid disorders.

Age and symptom duration

Another important aspect to consider is the effects of age and symptom duration in PIU. Although we did not find a moderating effect of participant age on the cognitive findings, most studies in this meta-analysis were conducted in relatively young participants. Excessive use of the internet can occur in older people,Reference Ioannidis, Treder, Chamberlain, Kiraly, Redden and Stein14 and this is a neglected area of research. Studies did not generally report symptom duration, so the current analysis cannot evaluate the extent to which cognitive problems may pre-date symptoms (perhaps reflecting vulnerability) as opposed to arising because of chronic engagement with internet-related activities. A longitudinal (3-months) exposure of smartphone-naive young adults to heavy smartphone use found it resulted in performance decrease in arithmetic accuracy and increase in concern for appropriateness (a measure of tendencies to conform to group conformity pressures).Reference Hadar, Hadas, Lazarovits, Alyagon, Eliraz and Zangen63 Although these results are preliminary, they may demonstrate the capacity of PIU to cause cognitive and behavioural changes.

Limitations

We need to highlight that ~85% of the studies included in the meta-analysis were based in centres of predominantly Asian communities. This limits the generalisability of the results to a degree, nevertheless, there was no evidence from the moderator analysis that the geographical area of study had an impact on the observed cognitive effects. Previous work has established that PIU is a global issue,Reference Kuss, Griffiths, Karila and Billieux4 and our meta-analysis supports the notion that the neurocognitive signature of PIU is not influenced by ethnicity. This is in line with previous work, which found that the profiles of PIU were similar across two separate geographical and cultural settings (USA and South Africa).Reference Ioannidis, Chamberlain, Treder, Kiraly, Leppink and Redden11 In addition, IQ measures are known to influence neurocognitive performance, which means that IQ is a parameter which needs to be controlled for in comparison studies. However, only 22.5% of studies included direct measures of IQ, and therefore, it is unclear whether differences between participants with PIU and control participants may have been caused by differences in IQ. Robust research should include such measures in the future.

Some studies were excluded due to use of non-standard cognitive domains, use of non-standard variants of common neuropsychological tasks (those not enabling replication by other groups); or insufficient numbers of other papers in the given domain to facilitate meta-analysis (a full list of those are presented in supplementary Table 7). For example, a number of studies utilised variants of the Stroop test with internet-related stimuli; pooling effects of ‘Stroop’ studies and ‘internet Stroop’ studies was not scientifically justified, because they evaluate different cognitive processes (colour-word inhibition versus attentional bias for internet-related stimuli, the latter measured via a heterogeneous spread of stimulus types and methodological approaches). By excluding these studies we do not mean to suggest that they are not extremely relevant for understanding PIU; but rather, the technique of meta-analysis is not well suited to examining non-standardised cognitive tasks, and is not suitable when few independent studies exist for a given cognitive domain. Finally, we opted for a broad operational definition of PIU; however, we recognise that further research is needed to better define and characterise PIU and its composite behaviours.

Summary and recommendations for future studies

The current meta-analysis provides firm evidence that PIU (defined broadly and operationally) is associated with cognitive impairments in motor inhibitory control, working memory, Stroop attentional inhibition and decision-making. These findings were not moderated by age, gender, geographical location or by whether the predominant online activity was gaming or not. This analysis constitutes a vital first step towards a better understanding of PIU, supporting its existence as a biological plausible entity associated with dysfunction of frontostriatal brain circuitry, and with clinical implications for people affected by PIU. The extent to which the identified cognitive deficits were present prior to PIU, or rather stemmed from engaging in such problematic behaviours cannot be addressed within the confines of this cross-sectional data analysis. Longitudinal studies are needed to address the issue of direction of effect and causality. Based on cognitive findings in other settings, such as in the context of substance use and behavioural addiction (gambling), we theorise that some cognitive problems associated with PIU may constitute vulnerability markers; whereas others may be more associated with chronicity.Reference Chamberlain, Lochner, Stein, Goudriaan, van Holst and Zohar17

This analysis also serves to highlight vital next steps needed in future papers, to further elucidate the specificity of the findings and their nature (see Appendix). This should include clarification of the role of IQ, the specific problematic behaviours involved beyond gaming, comorbid disorders that were seldom screened for (ADHD, impulse-control disorders including gambling disorder), examining a broader range of ages and other cultural settings, and employing optimised designs to maximise study quality. The review also identifies several cognitive domains that have yet to be extensively or adequately examined in PIU, such as facial processing, set-shifting, verbal recall, sustained attention, discounting, reflection-impulsivity and executive planning.

Acknowledgements

We would like to thank authors of published papers included in this meta-analysis who responded to requests for additional information to enable the meta-analysis. Part of the research was carried out by K.I. during a Fellowship awarded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research & Care (CLAHRC) East of England at Cambridgeshire and Peterborough NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Supplementary material

Supplementary material is available online at https://doi.org/10.1192/bjp.2019.3.

Appendix

Recommendations for future cognitive investigations of problematic internet use

(a) Salient demographic characteristics of the sample (each study group) should be described including age, gender, education levels and ethnicity.

(b) Specific problematic behaviours on the internet should be included, as this enables diagnostic specification of type of problematic internet use, for example gaming, gambling, sex, shopping, social networking, streaming media.

(c) Group differences in general intelligence should be ruled out using a suitable IQ test.

(d) When considering cognitive tests to include in a study, due consideration should be given to validation of tests in other settings and how easy it would be for other groups to attempt to replicate the findings.

(e) When describing cognitive results, inclusion of mean, standard deviations and sample size in each group is extremely valuable. For example, when using graphs, this information should also be included in a footnote in precise numerical form.

(f) Co-occurring comorbidities should be identified including mainstream mental disorders but also impulse-control disorders using suitable screening and diagnostic methods.

References

1Grohol, JM. Future clinical directions: professional development, pathology, and psychotherapy on-line. In Psychology and the Internet: Intrapersonal, Interpersonal, and Transpersonal Implications (ed Gackenbach, J): 111–40. Academic Press, 1998.Google Scholar
2Gackenbach, J. Psychology and the Internet. Intrapersonal, Interpersonal, and Transpersonal Implications (1st edn). Academic Press, 1998.Google Scholar
3Young, KS. Internet addiction: the emergence of a new clinical disorder. Publ CyberPsychology Behav 1998; 1: 237–44.Google Scholar
4Kuss, DJ, Griffiths, MD, Karila, L, Billieux, J. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Des 2014; 20: 4026–52.Google Scholar
5Beard, KW, Wolf, EM. Modification in the proposed diagnostic criteria for internet addiction. CyberPsychology Behav 2001; 4: 377–83.Google Scholar
6Kardefelt-Winther, D. A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Comput Human Behav 2014; 31: 351–4.Google Scholar
7American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5). American Psychiatric Association, 2013.Google Scholar
8Shapira, NA, Lessig, MC, Goldsmith, TD, Szabo, ST, Lazoritz, M, Gold, MS, et al. Problematic internet use: proposed classification and diagnostic criteria. Depress Anxiety 2003; 17: 207–16.Google Scholar
9Aboujaoude, E. Problematic Internet use: an overview. World Psychiatry 2010; 9: 85–90.Google Scholar
10Pontes, HM. Investigating the differential effects of social networking site addiction and Internet gaming disorder on psychological health. J Behav Addict 2017; 6: 601–10.Google Scholar
11Ioannidis, K, Chamberlain, SR, Treder, MS, Kiraly, F, Leppink, E, Redden, S, et al. Problematic internet use (PIU): associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. J Psychiatr Res 2016; 83: 94–102.Google Scholar
12Bianchini, V, Cecilia, MR, Roncone, R, Cofini, V. Prevalence and factors associated with problematic internet use: an Italian survey among L'Aquila students. Riv Psichiatr 2017; 52: 90–3.Google Scholar
13Király, O, Griffiths, MD, Demetrovics, Z. Internet gaming disorder and the DSM-5: conceptualization, debates, and controversies. Curr Addict Reports 2015; 2: 254–62.Google Scholar
14Ioannidis, K, Treder, MS, Chamberlain, SR, Kiraly, F, Redden, SA, Stein, DJ, et al. Problematic internet use as an age-related multifaceted problem: evidence from a two-site survey. Addict Behav 2018; 81: 157–66.Google Scholar
15Block, JJ. Issues for DSM-V: internet addiction. Am J Psychiatry 2008; 165: 306–7.Google Scholar
16Zohar, J. Addiction, impulsivity and obsessive-compulsive disorder: new formulation revealing ancient wisdom. Isr Med Assoc J 2010; 12: 233.Google Scholar
17Chamberlain, SR, Lochner, C, Stein, DJ, Goudriaan, AE, van Holst, RJ, Zohar, J, et al. Behavioural addiction—A rising tide? Eur Neuropsychopharmacol 2016; 26: 841–55.Google Scholar
18Brand, M, Young, KS, Laier, C, Wölfling, K, Potenza, MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: an interaction of person-affect-cognition-execution (I-PACE) model. Neurosci Biobehav Rev 2016; 71: 252–66.Google Scholar
19Argyriou, E, Davison, CB, Lee, TTC. Response inhibition and internet gaming disorder: a meta-analysis. Addict Behav 2017; 71: 54–60.Google Scholar
20Chamberlain, SR, Redden, SA, Leppink, E, Grant, JE. Problematic internet use in gamblers: impact on clinical and cognitive measures. CNS Spectr 2017; 22: 495–503.Google Scholar
21Stroup, DF, Berlin, JA, Morton, SC, Olkin, I, Williamson, GD, Rennie, D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000; 283: 2008–12.Google Scholar
22Quintana, DS. From pre-registration to publication: a non-technical primer for conducting a meta-analysis to synthesize correlational data. Front Psychol 2015; 6: 1549.Google Scholar
23Egger, M, Davey Smith, G, Schneider, M, Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997; 315: 629–34.Google Scholar
24Duval, S, Tweedie, R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000; 56: 455–63.Google Scholar
25Chen, CY, Huang, MF, Yen, JY, Chen, CS, Liu, GC, Yen, CF. Brain correlates of response inhibition in internet gaming disorder. Psychiatry Clin Neurosci 2015; 69: 201–9.Google Scholar
26Ding, WN, Sun, JH, Sun, YW, Chen, X, Zhou, Y, Zhuang, ZG, et al. Trait impulsivity and impaired prefrontal impulse inhibition function in adolescents with internet gaming addiction revealed by a go/no-go fMRI study. Behav Brain Funct 2014; 10: 20.Google Scholar
27Dong, G, Lu, Q, Zhou, H, Zhao, X. Impulse inhibition in people with internet addiction disorder: electrophysiological evidence from a go/nogo study. Neurosci Lett 2010; 485: 138–42.Google Scholar
28Kim, M, Lee, TH, Choi, JS, Kwak, YB, Hwang, WJ, Kim, T, et al. Neurophysiological correlates of altered response inhibition in internet gaming disorder and obsessive-compulsive disorder: perspectives from impulsivity and compulsivity. Sci Rep 2017; 7: 41742.Google Scholar
29Ko, CH, Hsieh, TJ, Chen, CY, Yen, CF, Chen, CS, Yen, JY, et al. Altered brain activation during response inhibition and error processing in subjects with internet gaming disorder: a functional magnetic imaging study. Euro Arch Psychiatry Clin Neurosci 2014; 264: 661–72.Google Scholar
30Littel, M, van den Berg, I, Luijten, M, van Rooij, AJ, Keemink, L, Franken, IH. Error processing and response inhibition in excessive computer game players: an event-related potential study. Addict Biol 2012; 17: 934–47.Google Scholar
31Liu, GC, Yen, JY, Chen, CY, Yen, CF, Chen, CS, Lin, WC, et al. Brain activation for response inhibition under gaming cue distraction in internet gaming disorder. Kaohsiung J Med Sci 2014; 30: 43–51.Google Scholar
32Luijten, M, Meerkerk, GJ, Franken, IH, van de Wetering, BJ, Schoenmakers, TM. An fMRI study of cognitive control in problem gamers. Psychiatry Res 2015; 231: 262–68.Google Scholar
33Metcalf, O, Pammer, K. Impulsivity and related neuropsychological features in regular and addictive first person shooter gaming. Cyberpsychol Behav Social Netw 2014; 17: 147–52.Google Scholar
34Sun, DL, Chen, ZJ, Ma, N, Zhang, XC, Fu, XM, Zhang, DR. Decision-making and prepotent response inhibition functions in excessive internet users. CNS Spectr 2009; 14: 75–81.Google Scholar
35Yao, YW, Wang, LJ, Yip, SW, Chen, PR, Li, S, Xu, J, et al. Impaired decision-making under risk is associated with gaming-specific inhibition deficits among college students with internet gaming disorder. Psychiatry Res 2015; 229: 302–9.Google Scholar
36Zhou, ZH, Yuan, GZ, Yao, JJ, Li, C, Cheng, ZH. An event-related potential investigation of deficient inhibitory control in individuals with pathological Internet use. Acta Neuropsychiatrica 2010; 22: 228–36.Google Scholar
37Zhou, Z, Zhou, H, Zhu, H. Working memory, executive function and impulsivity in internet-addictive disorders: a comparison with pathological gambling. Acta Neuropsychiatr 2016; 28: 92–100.Google Scholar
38Zhou, Z, Zhu, H, Li, C, Wang, J. Internet addictive individuals share impulsivity and executive dysfunction with alcohol-dependent patients. Front Behav Neurosci 2014; 8: 288.Google Scholar
39Choi, JS, Park, SM, Lee, J, Hwang, JY, Jung, HY, Choi, SW, et al. Resting-state beta and gamma activity in internet addiction. Int J Psychophysiol 2013; 89: 328–33.Google Scholar
40Choi, S-W, Kim, H, Kim, G-Y, Jeon, Y, Park, S, Lee, J-Y, et al. Similarities and differences among Internet gaming disorder, gambling disorder and alcohol use disorder: a focus on impulsivity and compulsivity. J Behav Addict 2014; 3: 246–53.Google Scholar
41Li, H, Zou, Y, Wang, J, Yang, X. Role of stressful life events, avoidant coping styles, and neuroticism in online game addiction among college students: a moderated mediation model. Front Psychol 2016; 7: 1–11.Google Scholar
42Lim, J-A, Lee, J-Y, Jung, HY, Sohn, BK, Choi, S-W, Kim, YJ, et al. Changes of quality of life and cognitive function in individuals with Internet gaming disorder. Medicine (Baltimore) 2016; 95: e5695.Google Scholar
43Cai, C, Yuan, K, Yin, J, Feng, D, Bi, Y, Li, Y, et al. Striatum morphometry is associated with cognitive control deficits and symptom severity in internet gaming disorder. Brain Imag Behav 2016; 10: 12–20.Google Scholar
44Choi, JS, Park, SM, Roh, MS, Lee, JY, Park, CB, Hwang, JY, et al. Dysfunctional inhibitory control and impulsivity in internet addiction. Psychiatry Res 2014; 215: 424–28.Google Scholar
45Dong, G, Zhou, H, Zhao, X. Male internet addicts show impaired executive control ability: evidence from a color-word Stroop task. Neurosci Lett 2011; 499: 114–18.Google Scholar
46Dong, G, Lin, X, Zhou, H, Lu, Q. Cognitive flexibility in internet addicts: fMRI evidence from difficult-to-easy and easy-to-difficult switching situations. Addict Behav 2014; 39: 677–83.Google Scholar
47Dong, G, Hu, Y, Lin, X, Lu, Q. What makes internet addicts continue playing online even when faced by severe negative consequences? Possible explanations from an fMRI study. Biol Psychol 2013; 94: 282–89.Google Scholar
48Dong, G, Shen, Y, Huang, J, Du, X. Impaired error-monitoring function in people with internet addiction disorder: an event-related fMRI study. Euro Addict Res 2013; 19: 269–75.Google Scholar
49Dong, G, Li, H, Wang, L, Potenza, MN. Cognitive control and reward/loss processing in internet gaming disorder: results from a comparison with recreational internet game-users. Euro Psychiatry 2017; 44: 30–8.Google Scholar
50Dong, G, Hu, Y, Lin, X. Reward/punishment sensitivities among internet addicts: implications for their addictive behaviors. Prog Neuropsychopharmacol Biol Psychiatry 2013; 46: 139–45.Google Scholar
51Dong, G, Huang, J, Du, X. Enhanced reward sensitivity and decreased loss sensitivity in internet addicts: an fMRI study during a guessing task. J Psychiatr Res 2011; 45: 1525–29.Google Scholar
52Wang, H, Jin, C, Yuan, K, Shakir, TM, Mao, C, Niu, X, et al. The alteration of gray matter volume and cognitive control in adolescents with internet gaming disorder. Front Behav Neurosci 2015; 9: 64.Google Scholar
53Xing, L, Yuan, K, Bi, Y, Yin, J, Cai, C, Feng, D, et al. Reduced fiber integrity and cognitive control in adolescents with internet gaming disorder. Brain Res 2014; 1586: 109–17.Google Scholar
54Yuan, K, Yu, D, Cai, C, Feng, D, Li, Y, Bi, Y, et al. Frontostriatal circuits, resting state functional connectivity and cognitive control in internet gaming disorder. Addict Biol 2017; 22: 813–22.Google Scholar
55Yuan, K, Qin, W, Yu, D, Bi, Y, Xing, L, Jin, C, et al. Core brain networks interactions and cognitive control in internet gaming disorder individuals in late adolescence/early adulthood. Brain Struct Funct 2016; 221: 1427–42.Google Scholar
56Lorenz, RC, Krüger, JK, Neumann, B, Schott, BH, Kaufmann, C, Heinz, A, et al. Cue reactivity and its inhibition in pathological computer game players. Addict Biol 2013; 18: 134–46.Google Scholar
57Pawlikowski, M, Brand, M. Excessive internet gaming and decision making: do excessive world of warcraft players have problems in decision making under risky conditions? Psychiatry Res 2011; 188: 428–33.Google Scholar
58Qi, X, Du, X, Yang, Y, Du, G, Gao, P, Zhang, Y, et al. Decreased modulation by the risk level on the brain activation during decision making in adolescents with internet gaming disorder. Front Behav Neurosci 2015; 9: 296.Google Scholar
59Nikolaidou, M, Fraser, DS, Hinvest, N. Physiological markers of biased decision-making in problematic internet users. J Behav Addict 2016; 5: 510–17.Google Scholar
60King, DL, Delfabbro, PH. Is preoccupation an oversimplification? A call to examine cognitive factors underlying internet gaming disorder. Addiction 2014; 109: 1566–7.Google Scholar
61Pontes, HM, Griffiths, MD. Internet Gaming Disorder and its associated cognitions and cognitive-related impairments: a systematic review using PRISMA guidelines. Rev Argent Cienc Comport 2015; 7: 102–18.Google Scholar
62Chamberlain, SR, Ioannidis, K, Grant, JE. The impact of comorbid impulsive/compulsive disorders on problematic internet use. J Behav Addict 2018; 7; 269–79.Google Scholar
63Hadar, A, Hadas, I, Lazarovits, A, Alyagon, U, Eliraz, D, Zangen, A. Answering the missed call: initial exploration of cognitive and electrophysiological changes associated with smartphone use and abuse. PLoS One 2017; 12: e0180094.Google Scholar
Figure 0

Fig. 1 Search strategy followed for meta-analysis.

SST, stop-signal task; STROOP, Stroop attentional inhibition task; Go/No-Go, go/no-go motor inhibitory control task. Please note that some studies in the final set examined more than one domain included in the final analysis.
Figure 1

Table 1 Total pooled sample sizes and model estimate measures for different cognitive domains

Figure 2

Fig. 2 Forest plots for (a) motor inhibitory control cognitive domains; and (b) for Stroop inhibitory control, decision-making and working memory cognitive domains.

Forest plots for various cognitive domains of problematic internet use participants versus controls; effect sizes are Hedge's g; positive values indicate people with problematic internet use performed worse than controls. aEffect size for working memory domain here is reported uncorrected. RE, Random effects.
Figure 3

Fig. 3 Funnel plots by cognitive domain.

(a) Attentional inhibition (Stroop task) test for plot asymmetry: z = 1.77, P = 0.078; (b) motor inhibitory control (go/no-go task) test for plot asymmetry: z = 0.46, P = 0.64; (c) motor inhibitory control (stop-signal task) test for plot asymmetry: z = 0.43, P = 0.66; (d) decision-making test for plot asymmetry: z = 1.1, P = 0.27; (e) discounting test for plot asymmetry: z = –2.7670, P = 0.0057; (f) working memory test for plot asymmetry: z = 0.88, P = 0.37. Meta-analysis funnels plots by cognitive domain; z- and P-values reported from regression test for funnel plot asymmetry (mixed-effects meta-regression model). Evidence of publication bias identified in the domains of discounting and working memory. The trim and fill method was used although effect size changed only for working memory (as indicated by the dotted line (non-corrected effect size 0.51)).
Supplementary material: File

Ioannidis et al. supplementary material

Ioannidis et al. supplementary material
Download Ioannidis et al. supplementary material(File)
File 2 MB
Submit a response

eLetters

No eLetters have been published for this article.