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Abstract

Background

Internet gaming disorder (IGD) is included in the DSM-5 as a provisional diagnosis. Whether IGD should be regarded as a disorder and, if so, how it should be defined and thresholded have generated considerable debate.

Methods

In the current study, machine learning was used, based on regional and interregional brain features. Resting-state data from 374 subjects (including 148 IGD subjects with DSM-5 scores ≥5 and 93 IGD subjects with DSM-5 scores ≥6) were collected, and multivariate pattern analysis (MVPA) was employed to classify IGD from recreational game use (RGU) subjects based on regional brain features (ReHo) and communication between brain regions (functional connectivity; FC). Permutation tests were used to assess classifier performance.

Results

The results demonstrated that when using DSM-5 scores ≥5 as the inclusion criteria for IGD subjects, MVPA could not differentiate IGD subjects from RGU, whether based on ReHo or FC features or by using different templates. MVPA could differentiate IGD subjects from RGU better than expected by chance when using DSM-5 scores ≥6 with both ReHo and FC features. The brain regions involved in the default mode network and executive control network and the cerebellum exhibited high discriminative power during classification.

Discussion

The current findings challenge the current IGD diagnostic criteria thresholding proposed in the DSM-5, suggesting that more stringent criteria may be needed for diagnosing IGD. The findings suggest that brain regions involved in the default mode network and executive control network relate importantly to the core criteria for IGD.

Open access

Applying fairness in labeling various types of internet use disorders

Commentary on How to overcome taxonomical problems in the study of internet use disorders and what to do with “smartphone addiction”?

Journal of Behavioral Addictions
Authors: Jon D. Elhai, Haibo Yang and Jason C. Levine

Abstract

We comment on arguments about internet and smartphone use disorders by Montag, Wegmann, Sariyska, Demetrovics, and Brand (2020). Although not currently official diagnoses, we emphasize that for some individuals, excessive internet/smartphone use can have dangerous consequences. We discuss the challenges with ICD-11 codifying only internet gaming as an internet use-related disorder, neglecting other types of excessive internet users. Montag et al.'s approach to classifying a broader range of internet use disorders seems more fair than the current system in aiding individuals needing treatment resources for excessive internet use.

Open access
Journal of Behavioral Addictions
Authors: Julian Strizek, Josefine Atzendorf, Ludwig Kraus, Karin Monshouwer, Alexandra Puhm and Alfred Uhl

Abstract

Background

Not much is known about the correlation between gaming problems and substance use across different countries. This paper presents cross-national analyses of different gaming indicators and their relationship to substance use.

Methods

Based on data from the 2015 ESPAD study, differences in the relationship between gaming and substance use across 35 countries were analysed using multi-level logistic regression, using substance use as an individual level predictor, economic wealth as a country-level predictor and a combined problem gaming indicator as the outcome.

Results

Multi-level logistic regressions revealed significant correlations between individual substance use and gaming problems, which varied across countries and were moderated by economic wealth. Students who used alcohol, tobacco or cannabis and who lived in high-income countries had a smaller risk of scoring positively on a combined problem gaming indicator than students who used alcohol, tobacco or cannabis and who lived in less prosperous countries.

Discussion

Different gaming indicators varied substantially across countries, with self-perceived gaming problems being more common in countries with a low prevalence of gaming. Significant cross-level effects demonstrate the need to take the societal context into account when the relationship between problem gaming and substance use is analysed. Prevention measures need to take the fact into account that patterns of substance use among problem gamers vary across countries.

Open access

Abstract

Objectives: The aim of the study is to adapt the Maladaptive Daydreaming Scale (MDS-16) to Hungarian, assess its psychometric properties, and establish its cut-off score. In addition, the relationship between maladaptive daydreaming and adverse childhood experiences was examined. Method: Study participants were recruited online via snowball sampling. Based on three inclusion criteria (self-identified MDer status; control over daydreaming; frequency of daydreaming) 160 out of 494 respondents were included in the study. Results: Our results confirm both the high reliability and convergent validity of the questionnaire. The cut-off score of 60 percentiles can reliably discriminate between excessive and normal daydreamers. The general applicability of the MDS-16-HU was tested and confirmed by the use of the Adverse Childhood Experience Questionnaire (ACE-10), a short, self-report questionnaire. Its results showed that certain types of childhood adversities increase the likelihood of maladaptive daydreaming. Conclusions: The instrument is a valid and reliable measure, therefore it can serve as a useful screening tool in clinical practice. In addition, our findings highlighted the role of childhood adversities in the aetiology of maladaptive daydreaming.

Open access

Abstract

Background and aims

Interest surrounding the relationship between flow and problematic gameplay has surged. An important antecedent of flow in the context of video-gaming is the skill-challenge balance, but researchers have only manipulated this balance by changing speed of play. The current research seeks to examine the skill-challenge balance and flow in a mobile game in which challenge is increased via the complexity of puzzles. We predicted games like Candy-Crush would more strongly support a model of flow in which the greatest flow would be experienced by more skilled players and that high flow games would induce the most urge to continue play.

Methods

We had 60 Candy-Crush players play games near their level standing (maximal skill-challenge balance), or games that were too easy or too hard. Perceived skill, challenge, flow, and urge to continue gameplay were measured after each game.

Results

Players felt the highest degree of skill-challenge balance when playing games around their level standing. Easy games produced the least flow, while both regular and hard games produced comparable flow despite hard games being far more challenging and frustrating. The findings support models of flow positing those with highest perceived skill will experience greater flow. Finally, flow and arousal combine to increase urge to keep playing.

Discussion and conclusions

Our findings suggest those with high perceived skill will experience deep, immersive flow which motivates players to keep playing.

Open access
Journal of Behavioral Addictions
Authors: Davide Marengo, Cornelia Sindermann, Daniela Häckel, Michele Settanni, Jon D. Elhai and Christian Montag

Abstract

Background and aims

Personality is one of the most frequently investigated variables to shed light on the putatively addictive use of the smartphone. By investigating associations between personality and individual differences in addictive smartphone use, researchers aim to understand if some personality traits predispose technology users to develop addictive behaviors. Here, based on existing empirical literature, we aimed at determining the strength of associations between Big Five personality traits and smartphone use disorder (SmUD) by a meta-analytic approach.

Method

For each Big Five personality trait, we performed a meta-analysis of correlations representing their association with SmUD. We also investigated possible publication bias and the moderating effects of age, gender, nationality, length of personality assessments, and time of publication.

Results

We found n = 26 eligible studies. In line with both the Interaction of Person-Affect-Cognition-Execution (I-PACE) model and the framework on problematic mobile-phone use by Billieux, we observed a positive association between Neuroticism and SmUD (r = 0.25), while the association between Extraversion and SmUD was not significant. Partially in line with the aforementioned theoretical frameworks, Conscientiousness was negatively associated with SmUD (r = −0.16). Remaining traits showed smaller associations. No significant publication bias emerged. Moderator analyses showed that time of publication moderated the link between Conscientiousness and SmUD. Moreover, Agreeableness and Conscientiousness showed a heightened inverse association with SmUD among older samples.

Conclusions

The present meta-analysis provides robust empirical evidence that Big Five personality traits can help to understand individual differences in SmUD, supporting the usefulness of their assessment when planning and targeting interventions aimed at at-risk individuals.

Open access
Journal of Behavioral Addictions
Authors: Afework Tsegaye, Joachim Bjørne, Anita Winther, Gyöngyi Kökönyei, Renáta Cserjési and H.N. Alexander Logemann

Abstract

Background and aims

Previous studies suggest that attentional bias and disengagement may vary as a function of Body Mass Index (BMI), most notably in a palatable food related context. Though this could indeed represent a food context specific effect, it could also represent a general reward related context effect. In addition, though mindfulness and stress have both been reported to affect attention, it is not yet clear whether these moderate the relationship between BMI and attention as a function of reward context. In the current study we addressed these questions. It was hypothesized that BMI would be positively associated with bias in a food context and money context relative to a neutral context. The inverse was expected for disengagement. It was expected that mindfulness would decrease these relationships and for stress the inverse was expected.

Methods

In the current online study, eighty-seven participants (24 males and 63 females; age: M = 30.1, SD = 8.3; BMI: M = 24.2, SD = 4.67), filled out questionnaires and completed a visuospatial cueing task measuring attention and disengagement of attention in a neutral, food-related, and money-related condition.

Results

There was no association between BMI and attentional bias. Higher BMI was associated with faster responses to money pictures presented opposite to a cued location as compared to money pictures that did not follow a predictive cue. Our results do not support a clear moderating role of mindfulness and stress.

Discussion and conclusion

Our results imply faster processing and associated quicker responding to unanticipated reward-related stimuli in individuals with overweight or obesity.

Open access

Abstract

Objective

In 2013, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) included the diagnostic criteria of Internet gaming disorder (IGD). Then, in 2019, the 11th Revision of the International Classification of Diseases (ICD-11) categorized gaming disorder (GD) as an addictive disorder. This review aimed to review the raised concerns, debate, and research of IGD or GD criteria and provide suggestions to resolve them.

Methods

A narrative review was conducted, and PubMed was searched for articles mentioning concerns and research on the DSM-5 criteria for IGD, ICD-11 criteria for GD, or criteria for other synonyms, such as problematic gaming or gaming addiction. A total of 107 articles were identified.

Results

Concerns were organized into three categories: conceptual framework, moral panic, and diagnostic validity. Most argumentations supported the proposition that GD and other substance use disorders have similar presentations. A clear definition of GD and adequate public education could prevent rather than exacerbate moral panic. Several researchers reported concerns regarding the nosology, diagnostic validity, and wording of each criterion. However, the threshold, five of the nine criteria with impaired function, demonstrated adequate validity in interview studies.

Conclusion

The current findings support the addiction framework, functional impairment, and validity of the GD criteria. However, further prospective, experimental, and clinical studies validating these findings are warranted. Moreover, an integrative review or debate conference could contribute to the organization of the available results and concept development. Aggregating adequate scientific information could allay or resolve concerns related to the diagnosis of GD.

Open access

Internet use disorders: What's new and what's not?

Commentary on: How to overcome taxonomical problems in the study of Internet use disorders and what to do with “smartphone addiction”? (Montag et al., 2019)

Journal of Behavioral Addictions
Author: Mark D. Griffiths

Abstract

This commentary critiques the recent paper by Montag et al. (2019) and (i) argues that there are a number of issues that are presented as contemporary but have been discussed in the internet addiction literature for over 20 years, (ii) argues that generalized internet use disorder (IUD)/smartphone use disorder (SmUD) and specific IUD/SmUD may mean different things to different scholars, (iii) suggests that online activities that involve content creation often utilize nonmobile devices, and (iv) suggests that there are some potentially problematic online behaviors that are not included as major activities in the proposed in Montag et al.‘s taxonomy of internet-related problematic behaviors.

Open access

Abstract

Background and aims

Problem gambling among adolescents has recently attracted attention because of easy access to gambling in online environments and its serious effects on adolescent lives. We proposed a machine learning-based analysis method for predicting the degree of problem gambling.

Methods

Of the 17,520 respondents in the 2018 National Survey on Youth Gambling Problems dataset (collected by the Korea Center on Gambling Problems), 5,045 students who had gambled in the past 3 months were included in this study. The Gambling Problem Severity Scale was used to provide the binary label information. After the random forest-based feature selection method, we trained four models: random forest (RF), support vector machine (SVM), extra trees (ETs), and ridge regression.

Results

The online gambling behavior in the past 3 months, experience of winning money or goods, and gambling of personal relationship were three factors exhibiting the high feature importance. All four models demonstrated an area under the curve (AUC) of >0.7; ET showed the highest AUC (0.755), RF demonstrated the highest accuracy (71.8%), and SVM showed the highest F1 score (0.507) on a testing set.

Discussion

The results indicate that machine learning models can convey meaningful information to support predictions regarding the degree of problem gambling.

Conclusion

Machine learning models trained using important features showed moderate accuracy in a large-scale Korean adolescent dataset. These findings suggest that the method will help screen adolescents at risk of problem gambling. We believe that expandable machine learning-based approaches will become more powerful as more datasets are collected.

Open access