Toward a delineation of the differences between high engagement and problem gaming

Problematic social media use in 3D? Relationships between traditional social media use, social virtual reality (VR) use, and mental health

This research expanded on prior work exploring the relationship between social media use, social support, and mental health by including the usage of social virtual reality (VR). In Study 1 (undergraduate students; n = 448) we examined divergent relationships between problematic social media use (e.g., Facebook, TikTok), total use, and users' mental health indicators (e.g., depression, anxiety, social isolation). To determine whether problematic social media use patterns extended to immersive 3-D environments, we sampled active social VR users (e.g., Rec Room) in Study 2 (n = 464). Problematic social VR use was related to decreased real-life social support (β = -.62, 95%CI [-.80, -.44]), but not to VR social support (β = -.06, 95%CI [-.25, .14]). Conversely, the amount of social VR use was only related to increased social VR (β = .06, 95%CI [.04, .15]) but not to real-life social support (β = -.02, 95%CI [-.05, .04]). Study 2 also revealed a finding that may be unique to the 3-D immersive environment: the amount of social VR use facilitated better mental health for VR users, but only through stronger perceived social support on social VR but not in real life. This result highlights the potential of immersive media to promote mental well-being by facilitating engaging and meaningful social interactions.

The Gaming Disorder Identification Test (GADIT) - A screening tool for Gaming Disorder based on ICD-11

Background

Gaming Disorder was included as an addictive disorder in the latest version of the International Classification of Diseases (ICD-11), published in 2022. The present study aimed to develop a screening tool for Gaming Disorder, the Gaming Disorder Identification Test (GADIT), based on the four ICD-11 diagnostic criteria: impaired control, increasing priority, continued gaming despite harm, and functional impairment.

Method

We reviewed 297 questionnaire items from 48 existing gaming addiction scales and selected 68 items based on content validity. Two datasets were collected: 1) an online panel (N = 803) from Australia, United States, United Kingdom and Canada, split into a development set (N = 589) and a validation dataset (N = 214); and 2) a university sample (N = 408) from Australia. Item response theory and confirmatory factor analyses were conducted to select eight items to form the GADIT. Validity was established by regressing the GADIT against known correlates of Gaming Disorder.

Results

Confirmatory factor analyses of the GADIT showed good model fit (RMSEA=<0.001-0.108; CFI = 0.98-1.00), and internal consistency was excellent (Cronbach's alphas = 0.77-0.92). GADIT scores were strongly associated with the Internet Gaming Disorder Test (IGDT-10), and significantly associated with gaming intensity, eye fatigue, hand pain, wrist pain, back or neck pain, and excessive in-game purchases, in both the validation and the university sample datasets.

Conclusion

The GADIT has strong psychometric properties in two independent samples from four English-speaking countries collected through different channels, and shown validity against existing scales and variables that are associated with Gaming Disorder. A cut-off of 5 is tentatively recommended for screening for Gaming Disorder.

Using machine learning algorithms and techniques for defining the impact of affective temperament types, content search and activities on the internet on the development of problematic internet use in adolescents' population

Background

By using algorithms and Machine Learning - ML techniques, the aim of this research was to determine the impact of the following factors on the development of Problematic Internet Use (PIU): sociodemographic factors, the intensity of using the Internet, different contents accessed on the Internet by adolescents, adolescents' online activities, life habits and different affective temperament types.

Methods

Sample included 2,113 adolescents. The following instruments were used: questionnaire about: socio-demographic characteristics, intensity of the Internet use, content categories and online activities on the Internet; Facebook (FB) usage and life habits; The Internet Use Disorder Scale (IUDS). Based on their scores on the scale, subjects were divided into two groups - with or without PIU; Temperament Evaluation of Memphis, Pisa, Paris, and San Diego scale for adolescents (A-TEMPS-A).

Results

Various ML classification models on our data set were trained. Binary classification models were created (class-label attribute was PIU value). Models hyperparameters were optimized using grid search method and models were validated using k-fold cross-validation technique. Random forest was the model with the best overall results and the time spent on FB and the cyclothymic temperament were variables of highest importance for these model. We also applied the ML techniques Lasso and ElasticNet. The three most important variables for the development of PIU with both techniques were: cyclothymic temperament, the longer use of the Internet and the desire to use the Internet more than at present time. Group of variables having a protective effect (regarding the prevention of the development of PIU) was found with both techniques. The three most important were: achievement, search for contents related to art and culture and hyperthymic temperament. Next, 34 important variables that explain 0.76% of variance were detected using the genetic algorithms. Finally, the binary classification model (with or without PIU) with the best characteristics was trained using artificial neural network.

Conclusion

Variables related to the temporal determinants of Internet usage, cyclothymic temperament, the desire for increased Internet usage, anxious and irritable temperament, on line gaming, pornography, and some variables related to FB usage consistently appear as important variables for the development of PIU.

Examining the relationship between non-suicidal self-harm and suicidality within the past 12-months and gaming problems in Norwegian full-time students

BACKGROUND

Although gaming problems are associated with poor mental health, few population-based studies have examined its association with self-harm and suicidality. This study investigates the association between gaming problems, non-suicidal self-harm and suicidality within the past year, stratified by sex among Norwegian full-time students.

METHODS

Data derived from the Norwegian Students' Health and Wellbeing Study 2022 (N = 59,544). The respondents were categorized into non-gamers, recreational gamers, engaged gamers, problematic gamers, and addicted gamers based on the Game Addiction Scale for Adolescents. Log-link binomial regression models, stratified by sex, adjusted for age, were used to estimate the risk ratio of non-suicidal self-harm (ideation and behavior) and suicidal behaviors (ideation and attempt) across different levels of gaming problems.

RESULTS

Among females, the risk of non-suicidal self-harm and suicidal ideation increased from non-gamer to problem gamer, with no differences between problem and addicted gamers. Among males, the risk of non-suicidal self-harm increased from non-gamers to engaged gamers, but no differences were observed between engaged, problematic, and addicted gamers. No sex × gaming category interaction was observed for suicide attempts. Engaged and addicted gamers had higher risks of suicide attempt than non-gamers and recreational gamers.

CONCLUSIONS

Gaming problems are associated with increased risk of non-suicidal self-harm and suicidal ideation among females. Among males, no differences were observed between engaged, problem and addicted gamers. The results highlight sex when studying health related outcomes and their association to level of gaming problems. Longitudinal studies are warranted to uncover the temporal mechanisms between IGD, non-suicidal self-harm and suicidality.

Screen time and addictive use of gaming and social media in relation to health outcomes

Introduction

This study examined associations between screen time and addictive use (i.e., heavy involvement and negative consequences) of gaming and social media, and their independent effects on health outcomes.

Methods

Survey data were collected from 2,265 participants (mean age = 21.57). Internet Gaming Disorder (IGD) and Social Media Disorder (SMD) were measured with the Gaming and Social Media Questionnaire (GSMQ-9), with separate measures for heavy involvement and negative consequences. Screen time was measured by weekly hours of gaming and social media. Assessed health outcomes were psychological problems, low self-concept, social problems, sleep problems, and sleep time.

Results

Screen time and addictive use were significantly associated for both gaming and social media, with associations being stronger for symptoms of heavy involvement compared to symptoms of negative consequences. However, despite significant associations, a substantial proportion of the participants with a high screen time did not meet any or just one symptom of addiction. More importantly, it was primarily negative consequences that had independent effects on health outcomes, except for sleep. High levels of heavy involvement in gaming, were even related to lower, not higher, levels of psychological problems.

Conclusion

The present findings study show that screen time is a poor indicator of addictive use of gaming and social media. Given that it was primarily negative consequences of gaming or social media that had effects on health outcomes, our study also emphasizes the need to distinguish between different types of addictive use and to further examine the diagnostic validity of the nine IGD symptom criteria.

Has the prevalence of problematic social media use increased over the past seven years and since the start of the COVID-19 pandemic? A meta-analysis of the studies published since the development of the Bergen social media addiction scale

Problematic Social Media Use (PSMU) has been defined as the lack of regulation of one's use of social media associated with negative outcomes in everyday functioning. Previous meta-analyses reported PSMU prevalence before the beginning of the COVID-19 pandemic and based the estimates on the cut-off scores, which are debatable in the current fields. The present meta-analysis aims to explore whether PSMU, as assessed by the most used self-report scale (i.e., the Bergen Social Media Addiction Scale; BSMAS), increased across the world (i) since the first published study on this topic (i.e., in the last seven years), (ii) since the beginning of the COVID-19 pandemic, and (iii) depending on age, gender and the income level of the countries. The meta-analysis involved 139 independent samples with 133,955 respondents from 32 countries spanning seven world regions. The results show that PSMU: (i) is significantly higher in low-income countries (LIC); (ii) did not increase in the last 7 years overall and after the beginning of the COVID-19 pandemic, except for LIC; (iii) did not vary depending on age and gender. The higher prevalence of PSMU reported during the COVID-19 pandemic by some single studies may have been a transient phenomenon to cope with physical distances in some countries, whilst, in others, an overall increase of PSMU occurred. High levels of PSMU in LIC might be explained based on socio-cultural differences between countries, but also with the higher prevalence of mental disorders in LIC since PSMU might be a symptom of other, more primary psychiatric disorders.

Pilot data findings from the Gothenburg treatment for gaming disorder: a cognitive behavioral treatment manual

Background

Gaming disorder (GD) is a new diagnosis included in the latest edition of the International Classification of Disease -11. Recently conducted international studies suggest a prevalence rate close to 2% for GD, highlighting the need for effective treatments for this patient population. Internationally there are few studies investigating effective treatments specifically designed for this condition. In this pilot study, we wanted to test a newly developed method, the Gothenburg Treatment for Gaming Disorder (GOT-TO-GO) manual; a 15-week cognitive behavioral therapy treatment for GD.

Method

This study utilized a single group design with pretest, post-test and a three- and six-month follow-up, with measures of severity of GD and mood. The participants (n = 28) were treatment-seeking adults with GD, aged 17 to 49 years.

Results

The results show a statistically significant decrease in symptoms of GD after treatment. Hours of gaming per week also decreased concomitantly with a 100% increase in non-gaming leisure hours. The decrease in symptoms of GD was maintained at the 3-months follow-up after treatment. Correspondingly we saw a decrease in both depression and anxiety that also was upheld 3 months after treatment.

Conclusion

As GD is a new diagnostic concept more research is needed, also taking psychiatric comorbidity into consideration, to arrive at evidence-based conclusions regarding effective treatments. Considering the promising results in this small pilot study with large behavioral changes and reduced symptoms of GD, upheld at least 3 months after treatment, a larger randomized controlled study is warranted.Clinical Trial Registration: https://www.clinicaltrials.gov/ct2/show/study/NCT05328596?term=NCT05328596&draw=2&rank=1, identifier NCT05328596.