Lost in the chaos: Flawed literature should not generate new disorders

The paper by Kuss, Griffiths, and Pontes (2016) titled “Chaos and confusion in DSM-5 diagnosis of Internet Gaming Disorder: Issues, concerns, and recommendations for clarity in the field” examines issues relating to the concept of Internet Gaming Disorder. We agree that there are serious issues and extend their arguments by suggesting that the field lacks basic theory, definitions, patient research, and properly validated and standardized assessment tools. As most studies derive data from survey research in functional populations, they exclude people with severe functional impairment and provide only limited information on the hypothesized disorder. Yet findings from such studies are widely used and often exaggerated, leading many to believe that we know more about the problem behavior than we do. We further argue that video game play is associated with several benefits and that formalizing this popular hobby as a psychiatric disorder is not without risks. It might undermine children’s right to play or encourage repressive treatment programs, which ultimately threaten children’s right to protection against violence. While Kuss et al. (2016) express support for the formal implementation of a disorder, we argue that before we have a proper evidence base, a sound theory, and validated assessment tools, it is irresponsible to support a formal category of disorder and doing so would solidify a confirmatory approach to research in this area

Addressing problematic video game use: A multimethod, dual-context perspective on leisure-time use : Commentary on: Policy responses to problematic video game use: A systematic review of current measures and future possibilities (Király et al., 2018)

A more integrative approach to the prevention of problematic gaming behavior is recommended in Király et al.’s review. We discuss the Dutch policy responses to problematic gaming behavior and suggest two alternatives to the dominant survey research approach to achieve this. Employing time-use/diary studies allows us to map out the full scope of leisure-time use and employing log-data analysis improves our understanding of gamer behavior within the virtual context. All of these approaches would benefit from accounting for the diversity of within-virtual context behavior. The approach is summarized as a multimethod, dual-context approach to understanding leisure-time behavior

4th International Conference on Behavioral Addictions February 20–22, 2017 Haifa, Israel

Background and aims: Previous small-scale studies in Portugal suggested that a minority of adolescents could be at risk for developing technological addictions such as Internet addiction (IA) and Internet Gaming Disorder (IGD). However, these findings are still to be replicated by larger studies using more robust samples. In light of this, the present aimed to ascertain how IA, IGD, and Facebook addiction (FA) may impact on Portuguese school-aged adolescents' mental health. Methods: A total of 509 school -aged adolescents were recruited (M age = 13 years; S.D. = 1.64 years) to a multi-technological addictions project. Data were collected on participants' sociodemographic, IA, IGD, FA, and several psychiatric symptoms. Results: Overall, the incidence of technological addictions was not overly prevalent across the sample (0% IA; 1% IGD; 1.8% FA), and a small minority of adolescents appeared to be 'at-risk' for developing technological addictions (1.6% IA; 0.6% IGD; 2.4% FA). Finally, increased symptoms of depression, anxiety, and stress were found to be consistently associated with augmented symptomatology of technological addictions. Conclusions: The findings of the present study provided preliminarily insights into how technological addictions may affect Portuguese school-aged adolescents, and corroborated a large body of international studies that found systematic links between technological addictions and psychiatric comorbidities

The Internet addiction components model and personality: Establishing construct validity via a nomological network

There is growing concern over excessive and sometimes problematic Internet use. Drawing upon the framework of the components model of addiction (Griffiths, 2005), Internet addiction appears as behavioural addiction characterised by the following symptoms: salience, withdrawal, tolerance, mood modification, relapse and conflict. A number of factors have been associated with an increased risk for Internet addiction, including personality traits. The overall aim of this study was to establish the association between personality traits and the Internet addiction components model in order to develop a theoretical framework via a nomological network. Internet addiction and personality traits were assessed in two independent samples of 3,105 adolescents in the Netherlands and 2,257 university students in England. The results indicate that low agreeableness and high neuroticism/low emotional stability are associated the Internet addiction components factor in both samples. However, low conscientiousness and low resourcefulness predicted it in the adolescent sample only. The implications include the usage of the Internet addiction components model as parsimonious tool for the initial screening of potential clients in mental health institutes, and identifying populations at risk through their personality traits which may prove advantageous for the initiation of targeted preventions efforts

Behavioural Addiction Open Definition 2.0 – Using the open science framework for collaborative and transparent theoretical development

Commentary to: How can we conceptualize behavioural addiction without pathologizing common behaviours?</p

Assessing Internet addiction using the parsimonious Internet addiction components model - a preliminary study [forthcoming]

Internet usage has grown exponentially over the last decade. Research indicates that excessive Internet use can lead to symptoms associated with addiction. To date, assessment of potential Internet addiction has varied regarding populations studied and instruments used, making reliable prevalence estimations difficult. To overcome the present problems a preliminary study was conducted testing a parsimonious Internet addiction components model based on Griffiths’ addiction components (2005), including salience, mood modification, tolerance, withdrawal, conflict, and relapse. Two validated measures of Internet addiction were used (Compulsive Internet Use Scale [CIUS], Meerkerk et al., 2009, and Assessment for Internet and Computer Game Addiction Scale [AICA-S], Beutel et al., 2010) in two independent samples (ns = 3,105 and 2,257). The fit of the model was analysed using Confirmatory Factor Analysis. Results indicate that the Internet addiction components model fits the data in both samples well. The two sample/two instrument approach provides converging evidence concerning the degree to which the components model can organize the self-reported behavioural components of Internet addiction. Recommendations for future research include a more detailed assessment of tolerance as addiction component

A weak scientific basis for gaming disorder: let us err on the side of caution

We greatly appreciate the care and thought that is evident in the 10 commentaries that discuss our debate paper, the majority of which argued in favor of a formalized ICD-11 gaming disorder. We agree that there are some people whose play of video games is related to life problems. We believe that understanding this population and the nature and severity of the problems they experience should be a focus area for future research. However, moving from research construct to formal disorder requires a much stronger evidence base than we currently have. The burden of evidence and the clinical utility should be extremely high, because there is a genuine risk of abuse of diagnoses. We provide suggestions about the level of evidence that might be required: transparent and preregistered studies, a better demarcation of the subject area that includes a rationale for focusing on gaming particularly versus a more general behavioral addictions concept, the exploration of non-addiction approaches, and the unbiased exploration of clinical approaches that treat potentially underlying issues, such as depressive mood or social anxiety first. We acknowledge there could be benefits to formalizing gaming disorder, many of which were highlighted by colleagues in their commentaries, but we think they do not yet outweigh the wider societal and public health risks involved. Given the gravity of diagnostic classification and its wider societal impact, we urge our colleagues at the WHO to err on the side of caution for now and postpone the formalization

Gameadvies Op Maat 2.0 (GOM2.0) - Dataset

AIMS: cross validation of self-report scales to improve the self-test on gameninfo.nl (now using: VAT scale) by exploring new scales and drafting a new instrument from those scales. RECRUITMENT: online recruitment: game journalism, Facebook & game information site (gameninfo.nl). Recruitment in two countries: Dutch Belgian (Flemish) and The Netherlands. Dutch sample: 431 cases. Flemish (Belgian) sample: 312 cases. Together: 743 cases. TIME: Average participation time 20 minutes. Dropout: relatively ‘tough’ list due to repetition of multiple problematic gaming measures: we lose multiple cases 282, ending up with a sample of approximately 461 cases. CLEANING: Removing cases that fail one of the two attentiveness questions: final total sample of 430 cases. SCALES: Digital Games Motivation Scale; Video Game Addiction Test (2012); Self Determination Theory Basic Needs; Clinical Video Game Addiction Test 2.0 (DSM-5 coverage); Depressive Mood Scale; Global Kids Online: Online Safety; Global kids Online: Excessive internet use [gaming]; Life Satisfaction; Mental Health Inventory-5; Digital ambitions (career as streamer or progamer); ICD-Gaming Disorder; Open Science Def. Behavioral Addictions; Attentiveness (data quality check item); Demographics: Age (years); Demographics: Education level; Demographics: Gender; Device choice; Favorite game; Gametime per session; IGD Disorder Scale Lemmens: persistence; IGD Disorder Scale Lemmens: tolerance; Time played per weekday (hours); Time played per weekend day (hours); Other favorite games (3 max); Days on which you play (mon, tues, etc.

Gameadvies Op Maat 2.0 (GOM2.0) - Dataset

AIMS: cross validation of self-report scales to improve the self-test on gameninfo.nl (now using: VAT scale) by exploring new scales and drafting a new instrument from those scales. RECRUITMENT: online recruitment: game journalism, Facebook & game information site (gameninfo.nl). Recruitment in two countries: Dutch Belgian (Flemish) and The Netherlands. Dutch sample: 431 cases. Flemish (Belgian) sample: 312 cases. Together: 743 cases. TIME: Average participation time 20 minutes. Dropout: relatively ‘tough’ list due to repetition of multiple problematic gaming measures: we lose multiple cases 282, ending up with a sample of approximately 461 cases. CLEANING: Removing cases that fail one of the two attentiveness questions: final total sample of 430 cases. SCALES: Digital Games Motivation Scale; Video Game Addiction Test (2012); Self Determination Theory Basic Needs; Clinical Video Game Addiction Test 2.0 (DSM-5 coverage); Depressive Mood Scale; Global Kids Online: Online Safety; Global kids Online: Excessive internet use [gaming]; Life Satisfaction; Mental Health Inventory-5; Digital ambitions (career as streamer or progamer); ICD-Gaming Disorder; Open Science Def. Behavioral Addictions; Attentiveness (data quality check item); Demographics: Age (years); Demographics: Education level; Demographics: Gender; Device choice; Favorite game; Gametime per session; IGD Disorder Scale Lemmens: persistence; IGD Disorder Scale Lemmens: tolerance; Time played per weekday (hours); Time played per weekend day (hours); Other favorite games (3 max); Days on which you play (mon, tues, etc.

The relationship between mental well-being and dysregulated gaming: A specification curve analysis of core and peripheral criteria in five gaming disorder scales

Gaming disorder (also known as dysregulated gaming) has received significant research and policy attention based on concerns that certain patterns of play are associated with decreased mental well-being and/or functional impairment. In this study, we use specification curve analysis to examine analytical flexibility and the strength of the relationship between dysregulated gaming and well-being in the form of general mental health, depressive mood, and life satisfaction. Dutch and Flemish gamers (n = 424) completed five unique dysregulated gaming measures (covering nine scale variants) and three well-being measures. We find a consistent negative relationship; across 972 justifiable regression models, the median standardized regression coefficient was –0.40 (min: –0.54, max: –0.19). Data show that the majority of dysregulated gaming operationalizations converge upon highly similar estimates of well-being (i.e. have similar concurrent validity). However, variance is introduced by the choice of well-being measure; results indicate that dysregulated gaming is more strongly associated with depressive mood than with life satisfaction. Weekly gametime accounted for little to no unique variance in well-being in the sample. We argue that research on this topic should compare a broad range of functional and well-being outcomes, and work to identify a maximally parsimonious of dysregulated gaming criteria. Given somewhat minute differences between dysregulated gaming scales when used in survey-based studies and largely equivalent relationships with mental health indicators, harmonization of measurement should be a priority