Gaming Disorder: The role of a gamers flow profile.

Digital games are widely popular and integral to contemporary entertainment. Nevertheless, a proportion of users present with disordered/excessive gaming behaviours, provisionally classified as Internet Gaming Disorder (IGD). Previous literature suggests examining the contribution of an individual's profile of immersive engagement with their gaming activity, known as online flow, for disordered gaming behaviours. Therefore, the main goals of this study were (1) to categorise gamers into distinct profiles based on their online flow experiences and (2) to investigate the differences in disordered gaming among these different flow profiles. A sample of 565 gamers (12-68 years, Mage = 29.3 years) was assessed twice over six months with the Online Flow Questionnaire (OFQ), the Internet Gaming Disorder Scale-Short-Form (IGDS9-SF), and the Gaming Disorder Test (GDT). Latent profile analysis (LPA) identified five distinct profiles encompassing 'High-Flow with High Loss of Control' (HF-HLOC; 14.0 %), 'Low Flow with Low Enjoyment' (LF-LE; 11.9 %), 'Average Flow with Low Enjoyment' (AF-LE; 17.5 %), 'Low Flow with High Enjoyment' (LF-HE; 20.2 %), and 'High Loss of Sense of Time with Low Loss of Control' groups (HLOT-LLOC; 36.5 %). As hypothesised, individuals across varying profiles evidenced differences in their concurrent and longitudinal disordered gaming behaviours. Overall, findings suggest that 'loss of sense of time' may be the most pivotal factor in differentiating flow states and profiles during gaming, advocating its consideration in disordered gaming assessment and treatment.

Pixels and perception: Mapping the association between digital media and psychotic-like experiences in adolescents.

INTRODUCTION: Psychotic-like experiences (PLEs) during adolescence can lead to psychotic disorders. Digital media usage has been suggested to link to PLEs, but research is limited on how different types of screen exposure may differentially relate to PLEs over time. This study aimed to examine longitudinal associations between screen usage patterns and PLEs in adolescents. METHODS: Participants comprised 11,876 adolescents assessed annually from ages 9-12 years as part of the Adolescent Brain Cognitive Development study (ABCD). Screen usage (TV, video games, online video, social media, texting, video chat) and PLEs were assessed via self-report. Longitudinal network analysis models were estimated to examine connections between screen usage types and PLEs across three time points. RESULTS: Two clusters were formed, including digital media for socializing (e.g., social media/texting/video chat) and digital media for entertainment (e.g., online video/video games/TV). Texting and online video(s) had the highest centrality at each time point, suggesting importance in the network. PLE symptoms of hallucinations and concentration difficulties exhibited higher centrality than other symptoms. Online video and TV were influential bridges between screen usage and PLEs. Network structure significantly differed between ages 9-10 and 10-12 years, but global strength was unchanged over time. DISCUSSION: Results highlight the importance of understanding the associations between specific screen usage types and PLE symptoms. Texting and online video usage appear most influential in the development of adolescent PLEs over time. Findings can inform targeted interventions to promote healthy screen habits and reduce PLEs in at-risk youth.

Examining how gamers connect with their avatars to assess their anxiety: A novel artificial intelligence approach.

Research has supported that a gamer's attachment to their avatar can offer significant insights about their mental health, including anxiety. To assess this hypothesis, longitudinal data from 565 adult and adolescent participants (Mage = 29.3 years, SD = 10.6) was analyzed at two points, six months apart. Respondents were assessed using the User-Avatar Bond (UAB) scale and the Depression Anxiety Stress Scale (DASS) to measure their connection with their avatar and their risk for anxiety. The records were processed using both untuned and tuned artificial intelligence [AI] classifiers to evaluate present and future anxiety. The findings indicated that AI models are capable of accurately and autonomously discerning cases of anxiety risk based on the gamers' self-reported UAB, age, and duration of gaming, both at present and after six months. Notably, random forest algorithms surpassed other AI models in effectiveness, with avatar compensation emerging as the most significant factor in model training for prospective anxiety. The implications for assessment, prevention, and clinical practice are discussed.

Translating the user-avatar bond into depression risk: A preliminary machine learning study.

Research has shown a link between depression risk and how gamers form relationships with their in-game figure of representation, called avatar. This is reinforced by literature supporting that a gamer's connection to their avatar may provide broader insight into their mental health. Therefore, it has been argued that if properly examined, the bond between a person and their avatar may reveal information about their current or potential struggles with depression offline. To examine whether the connection with an individuals' avatars may reveal their risk for depression, longitudinal data from 565 adults/adolescents (Mage = 29.3 years, SD = 10.6) were evaluated twice (six months apart). Participants completed the User-Avatar-Bond [UAB] scale and Depression Anxiety Stress Scale to measure avatar bond and depression risk. A series of tuned and untuned artificial intelligence [AI] classifiers analyzed their responses concurrently and prospectively. This allowed the examination of whether user-avatar bond can provide cross-sectional and predictive information about depression risk. Findings revealed that AI models can learn to accurately and automatically identify depression risk cases, based on gamers' reported UAB, age, and length of gaming involvement, both at present and six months later. In particular, random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor. Study outcomes demonstrate that UAB can be translated into accurate, concurrent, and future, depression risk predictions via trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these results.

Identifying self-disclosed anxiety on Twitter: A natural language processing approach.

BACKGROUND: Text analyses of social media posts are a promising source of mental health information. This study used natural language processing to explore distinct language patterns on Twitter related to self-reported anxiety diagnosis. METHODS: A total of 233.000 tweets made by 605 users (300 reporting anxiety diagnosis and 305 not) over six months were comparatively analysed, considering user behavior, Linguistic Inquiry Word Count (LIWC), and sentiment analysis. Twitter users with a self-disclosed diagnosis of anxiety were classified as 'anxious' to facilitate group comparisons. RESULTS: Supervised machine learning models showed a high prediction accuracy (Naïve Bayes 81.1 %, Random Forests 79.8 %, and LASSO-regression 79.4 %) in identifying Twitter users' self-disclosed diagnosis of anxiety. Additionally, a Latent Profile Analysis (LPA) identified four profiles characterized by high sentiment (31 % anxious participants), low sentiment (68 % anxious), self-immersed (80 % anxious), and normative behavior (38 % anxious). CONCLUSION: The digital footprint of self-disclosed anxiety on Twitter posts presented a high frequency of words conveying either negative sentiment, a low frequency of positive sentiment, a reduced frequency of posting, and lengthier texts. These distinct patterns enabled highly accurate prediction of anxiety diagnosis. On this basis, appropriately resourced, awareness raising, online mental health campaigns are advocated.

Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning.

Background and aims: Gaming disorder [GD] risk has been associated with the way gamers bond with their visual representation (i.e., avatar) in the game-world. More specifically, a gamer's relationship with their avatar has been shown to provide reliable mental health information about the user in their offline life, such as their current and prospective GD risk, if appropriately decoded. Methods: To contribute to the paucity of knowledge in this area, 565 gamers (Mage = 29.3 years; SD =10.6) were assessed twice, six months apart, using the User-Avatar-Bond Scale (UABS) and the Gaming Disorder Test. A series of tuned and untuned artificial intelligence [AI] classifiers analysed concurrently and prospectively their responses. Results: Findings showed that AI models learned to accurately and automatically identify GD risk cases, based on gamers' reported UABS score, age, and length of gaming involvement, both concurrently and longitudinally (i.e., six months later). Random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor. Conclusion: Study outcomes demonstrated that the user-avatar bond can be translated into accurate, concurrent and future GD risk predictions using trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these findings.

Mapping the links between sexual addiction and gambling disorder: A Bayesian network approach.

Contemporary literature and recent classification systems have expanded the field of addictions to include problematic behaviours such as gambling and sexual addiction. However, conceptualisation of behavioural addictions is poorly understood and gender-based differences have emerged in relation to how these behaviours are expressed. The current research conducted partial-correlation and Bayesian network analyses to assess the symptomatic structure of gambling disorder and sexual addiction. Convenience community sampling recruited 937 adults aged 18 to 64 years (315 females, Mage = 30.02; 622 males, Mage = 29.46). Symptoms of problematic behaviours were measured using the Online Gambling Disorder Questionnaire (OGDQ) and the Bergen Yale Sex Addiction Scale (BYSAS). Results indicate distinct gender-based differences in the symptom networks of sexual addiction and gambling disorder, with a more complex network observed amongst men for both conditions. Addiction salience, withdrawal and dishonesty/deception were important components of the addictive network. Interpersonal conflict was more central for women while intrapsychic conflict a more prominent issue for men. Differences in the two symptom networks indicate separate disorders as opposed to a single underlying construct. Treating practitioners and community initiatives aimed at addressing sexual addiction and disordered gambling should consider gender, when designing educational or therapeutic interventions.

Exploratory structural equation modeling: a streamlined step by step approach using the R Project software.

Confirmatory Factor Analysis (CFA) has been a popular yet limited approach to assessing latent factor structures. Despite items rarely loading exclusively on one latent factor in multifactorial scales, CFA assumes all indicators/items should load uniquely on their allocated latent dimensions. To address this weakness, Exploratory Structural Equation Modeling (ESEM) combines exploratory factor analyses (EFA) and CFA procedures, allowing cross-loadings to occur when assessing hypothesized models. Although such advantages have enhanced ESEM popularity, its adoption is often limited by software rigidity and complex coding difficulties. To address these obstacles, the current tutorial presents a streamlined, step-by-step approach using the open-source software R while providing both R and Mplus ESEM syntax. The tutorial demonstrates the sequence of the ESEM stages by examining the frequently debated Strengths and Difficulties Questionnaire (SDQ) factor structure, using openly accessible data from the Longitudinal Study of Australian Children (LSAC). As ESEM may allow a better understanding of the complex associations in multidimensional scales, this tutorial may optimize the epidemiological and clinical assessment of common yet multifaceted psychiatric presentations.

Conceptualising social media addiction: a longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults.

BACKGROUND: Problematic social media use has been identified as negatively impacting psychological and everyday functioning and has been identified as a possible behavioural addiction (social media addiction; SMA). Whether SMA can be classified as a distinct behavioural addiction has been debated within the literature, with some regarding SMA as a premature pathologisation of ordinary social media use behaviour and suggesting there is little evidence for its use as a category of clinical concern. This study aimed to understand the relationship between proposed symptoms of SMA and psychological distress and examine these over time in a longitudinal network analysis, in order better understand whether SMA warrants classification as a unique pathology unique from general distress. METHOD: N = 462 adults (Mage = 30.8, SDage = 9.23, 69.3% males, 29% females, 1.9% other sex or gender) completed measures of social media addiction (Bergen Social Media Addiction Scale), and psychological distress (DASS-21) at two time points, twelve months apart. Data were analysed using network analysis (NA) to explore SMA symptoms and psychological distress. Specifically, NA allows to assess the 'influence' and pathways of influence of each symptom in the network both cross-sectionally at each time point, as well as over time. RESULTS: SMA symptoms were found to be stable cross-sectionally over time, and were associated with, yet distinct, from, depression, anxiety and stress. The most central symptoms within the network were tolerance and mood-modification in terms of expected influence and closeness respectively. Depression symptoms appeared to have less of a formative effect on SMA symptoms than anxiety and stress. CONCLUSIONS: Our findings support the conceptualisation of SMA as a distinct construct occurring based on an underpinning network cluster of behaviours and a distinct association between SMA symptoms and distress. Further replications of these findings, however, are needed to strengthen the evidence for SMA as a unique behavioural addiction.

Social media use and abuse: Different profiles of users and their associations with addictive behaviours.

Introduction: Social media use has become increasingly prevalent worldwide. Simultaneously, concerns surrounding social media abuse/problematic use, which resembles behavioural and substance addictions, have proliferated. This has prompted the introduction of 'Social Media Addiction' [SMA], as a condition requiring clarifications regarding its definition, assessment and associations with other addictions. Thus, this study aimed to: (a) advance knowledge on the typology/structure of SMA symptoms experienced and: (b) explore the association of these typologies with addictive behaviours related to gaming, gambling, alcohol, smoking, drug abuse, sex (including porn), shopping, internet use, and exercise. Methods: A sample of 968 [Mage = 29.5, SDage = 9.36, nmales = 622 (64.3 %), nfemales = 315, (32.5 %)] adults was surveyed regarding their SMA experiences, using the Bergen Social Media Addiction Scale (BSMAS). Their experiences of Gaming, Internet, Gambling, Alcohol, Cigarette, Drug, Sex, Shopping and Exercise addictions were additionally assessed, and latent profile analysis (LPA) was implemented. Results: Three distinct profiles were revealed, based on the severity of one's SMA symptoms: 'low', 'moderate' and 'high' risk. Subsequent ANOVA analyses suggested that participants classified as 'high' risk indicated significantly higher behaviours related to internet, gambling, gaming, sex and in particular shopping addictions. Conclusions: Results support SMA as a unitary construct, while they potentially challenge the distinction between technological and behavioural addictions. Findings also imply that the assessment of those presenting with SMA behaviours, as well as prevention and intervention targeting SMA at risk groups, should consider other comorbid addictions.

The development and validation of the pandemic medication-assisted treatment questionnaire for the assessment of pandemic crises impact on medication management and administration for patients with opioid use disorders.

Pandemic and the globally applied restriction measures mainly affect vulnerable population groups, such as patients with opioid use disorders. Towards inhibiting SARS-Cov-2 spread, the medication-assisted treatment (MAT) programs follow strategies targeting the reduction of in-person psychosocial interventions and an increase of take-home doses. However, there is no available instrument to examine the impact of such modifications on diverse health aspects of patients under MAT. The aim of this study was to develop and validate the PANdemic Medication-Assisted Treatment Questionnaire (PANMAT/Q) to address the pandemic effect on the management and administration of MAT. In total, 463 patients under ΜΑΤ participated. Our findings indicate that PANMAT/Q has been successfully validated exerting reliability and validity. It can be completed within approximately 5 min, and its implementation in research settings is advocated. PANMAT/Q could serve as a useful tool to identify the needs of patients under MAT being at high risk of relapse and overdose.

Personality inventory for DSM-5-Brief Form (PID-5-BF): Measurement invariance across men and women.

The study examined the measurement invariance (configural, metric, scalar, and residual) of the Personality Inventory for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) Brief Form (PID-5-BF; Krueger et al., 2013) across gender for the theorized Five-Factor oblique model. A large group of adults (N = 502), with ages ranging from 18 to 67 years, from the Australian general community completed the PID-5-BF. When the Δχ² test was applied, multiple-group confirmatory factor analysis supported configural invariance and full metric and residual invariance. For scalar invariance, at least seven items (constituting 13 thresholds) across the different dimensions lacked invariance for one or more of its thresholds. In addition, controlling all these noninvariance thresholds, women had higher latent mean scores for Negative Affect, and men had higher scores for Antagonism and Disinhibition. When the Δcomparative fit index (ΔCFI) test was applied, the findings also supported the configural invariance model and the full metric and residual invariance models. With the exception of one threshold, all the other thresholds were also invariant. The psychometric and practical implications of the findings are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Psychometric properties of the Bergen Social Media Addiction Scale: An analysis using item response theory.

Background: Social media use has become an everyday behavior in contemporary life resulting in increased participation. A minority of individuals, especially younger adults, may engage excessively with the medium, resulting in the emergence of problematic social media use (PSMU). One way of assessing PSMU is by administering the Bergen Social Media Addiction Scale (BSMAS). The present study investigated the psychometric properties and prevalence of the BSMAS using Item Response Theory (IRT). Additionally, it evaluated risk factors such as gender and age. Methods: A relatively large community sample (N = 968, Mage  = 29.5 years, SD = 9.36, 32.5% women) completed the BSMAS online. Results: IRT analyses showed differences regarding the BSMAS items' discrimination, difficulty, and reliability capacities, with a raw score exceeding 26 (out of 30) indicating a higher risk of PSMU (n = 11; 1.1%). Females and younger participants were at greater risk of developing PSMU. Conclusion: The BSMAS functions as a reliable measure of PSMU, particularly between average to high levels of the trait. Additionally, younger participants were shown to be at higher risk of PSMU suggesting that prevention and intervention protocols should focus on this group.

Co-Occurrence of Gaming Disorder and Other Potentially Addictive Behaviours between Australia, New Zealand, and the United Kingdom.

Background: Evidence suggests that gamers can have varying experiences of disordered gaming behaviours due to coping mechanisms and how they can act as risk or protective factor in the development and/or maintenance of disordered behaviours. A particular area of interest is how this may manifest across different countries. Understanding the interplay of these potential risk and protective factors within different countries will aid identifying and preventing disordered behaviours. Methods: Three cohorts were recruited from Australia, New Zealand, and the United Kingdom. Each cohort was required to complete a battery of psychometric scales exploring problematic behaviours, problematic substance use, co-occurrence, coping styles, and personality. A latent profile analysis was conducted to examine the differences between cohorts and further investigated with additional analyses. Results: The findings suggested that a minority of gamers were affected by gaming disorder, and there appeared an at-risk cohort who utilise gaming as a maladaptive coping strategy. Other accompanying potentially addictive behaviour or substance use may be exacerbated as a result, the manifestation of which can be influenced by cultural elements. Conclusions: When considering gamers from countries which hold similar views, it is important to be cognisant of the variations found in the manifestations of disordered gaming and accompanying potentially addictive behaviours. This will allow for a more precise identification of at-risk behaviours, which will result in more favourable treatment outcomes for those who are considered at-risk or high-risk individuals.

Suicidal behaviors in the entertainment industry: a preliminary exploration of the interplay between work scheduling, social support, and wellbeing in Australia.

OBJECTIVE: Workers of the Australian entertainment industry exhibit disproportionately high rates of impaired psychological wellbeing and suicidal behaviors, with such rates being exacerbated by the negative impact of working long and odd hours (Work Scheduling Impact; WSI). Nonetheless, stable and secure social support networks may buffer the risks associated with such systemic difficulties. METHODS: The responses of 1302 Australian entertainment industry workers (50.3% females, Mage 38.39 years) on the Multidimensional Scale of Perceived Social Support, the Short Form Health Survey, WSI, and suicidal behaviors questions were examined via moderation analyses. RESULTS: Higher social support and lower WSI appeared to reduce the suicidal ideation of those experiencing poorer mental health, while lower WSI further enhanced social support's positive effect. CONCLUSIONS: Findings highlight the likely detrimental effect of WSI regarding the suicidal ideation reported by vulnerable Australian entertainment industry workers and stress the importance of the social support they experience. PUBLIC HEALTH IMPLICATIONS: Interventions attempting to increase social support could improve inherent conditions associated with the Australian entertainment industry. Similarly, the negative effect of working long and odd hours on workers' mental health and suicidal behaviors indicates the need to regulate the industry appropriately.

Inter-relationships between ADHD, ODD and impulsivity dimensions in emerging adults revealed by network analysis: extending the 'trait impulsivity hypothesis'.

Background: The trait-impulsivity hypothesis posits impulsivity as the underlying substrate of Attention-deficit/hyperactivity disorder (ADHD) and Oppositional defiant disorder (ODD) symptom expressions. The current study applied network analysis to evaluate the inter-relationships of dimensions within ADHD (inattention and hyperactivity/impulsivity) and ODD (anger/irritable, vindictiveness, and argumentative/defiant behavior) with components of impulsivity as captured by the UPPS-P model (negative urgency, lack of premeditation, lack of perseverance, sensation seeking, and positive urgency). Method: A total of 324 emerging adults (women = 246) from the general community completed questionnaires covering these dimensions. Results: Our findings showed that the ADHD and ODD dimensions were associated differentially with different types of impulsivity, in their unique patterns of network connectivities, a possibility that has had little attention in the trait-impulsivity hypothesis literature. Conclusions: This study is the first to tease out the unique associations of the ADHD and ODD dimensions with different types of impulsivity, and in that way provide new contributions to our understanding of the existing trait impulsivity theory. Our findings would be especially relevant to those interested in understanding how different dimensions of trait impulsivity underly the ADHD and ODD dimensions.

The Online Flow Questionnaire: An Item Response Theory Examination.

Online flow refers to the rate of an individual's absorbance in an Internet activity in which they are engaged. It has been implicated with both the effectiveness of cyberhealth and online education applications, as well as excessive Internet use. One way of assessing it is the Online Flow Questionnaire (OFQ). Consequently, this study investigated the optimum measurement of online flow, as well as OFQ cutoff points, using Item Response Theory (IRT). A large sample of high school students from Greece (N = 1579, Mage = 16.12, SDage = 0.85; 50.5 percent females) completed the OFQ and the data were analyzed using IRT procedures. The analysis indicated that items in the OFQ possess differing levels of discrimination and difficulty, although all items were generally within acceptable ranges. An OFQ cutoff score of 5 represented an extremely high level of online flow experiences. The findings suggest that the OFQ generally functions as an acceptable marker of overall online flow. However, the current format of the OFQ appears to inhibit its ability to detect variability at the extreme low and high ends of the latent trait in the population assessed. Future revisions (potentially employing computerized adaptive tests) of the OFQ response format may enhance its utility.

Cross-Addiction Risk Profile Associations with COVID-19 Anxiety: a Preliminary Exploratory Study.

"Cross-addiction" involves a person substituting one form of addictive behaviour for another. Indeed, cross-additive presentations have been frequently described (e.g. from drugs to alcohol, gambling to sex), and risk profiles have been assumed. Nevertheless, there has been a dearth of evidence considering the occurrence of cross-addiction risk profiles in the community. This research is imperative for informing effective prevention/intervention policies, especially under anxiety-provoking conditions, such as the current coronavirus pandemic. To address this need, a cross-sectional exploratory research design was utilized, with quantitative survey data obtained from 968 respondents (18-64; M age = 29.5 years, SD = 9.36), who completed an online survey regarding a range of addictive behaviours (i.e. abuse of alcohol, drug, smoking, online gaming, shopping, internet, exercise, online gambling, sex, and social media) and their anxiety about the coronavirus. Latent class/profiling analyses were implemented to (a) explore profiles of cross-addiction risk, (b) describe the characteristics and the proportions of these profiles, and (c) identify their differential associations with the pandemic precipitated anxiety. Findings revealed two distinct profiles/types, the "cross-addiction low risk" (57.4%) and the "cross-addiction high risk" (42.6%). Those in the latter scored consistently higher across all behaviours assessed, were more likely to suffer from concurrent addictive problems, and reported significantly higher levels of pandemic-related anxiety. Implications for prevention, assessment, and treatment and future research are discussed.