Adolescent Internet pornography viewing has been significantly increased in the last decade with research highlighting its association with Internet addiction (IA). However, there is little longitudinal data on this topic, particularly in relation to peer context effects. This study aimed to examine age- and context-related variations in the Internet pornography–IA association.
A total of 648 adolescents, from 34 classrooms, were assessed at 16 years and then at 18 years to examine the effect of Internet pornography preference on IA in relation to the classroom context. IA was assessed using the Internet Addiction Test (Young, 1998), Internet pornography preference (over other Internet applications) was assessed with a binary (yes/no) question, and classroom introversion and openness to experience (OTE) with the synonymous subscales within the Five Factor Questionnaire (Asendorpf & Van Aken, 2003).
Three-level hierarchical linear models were calculated. Findings showed that viewing Internet pornography exacerbates the risk of IA over time, while classroom factors, such as the average level of OTE and introversion, differentially moderate this relationship.
Discussion and conclusion
The study demonstrated that the contribution of Internet pornography preference (as an IA risk factor) might be increased in more extroverted classrooms and decreased in OTE classrooms.
The risk effect of anxiety on addictive behaviors, including Internet addiction (IA), has repeatedly been highlighted in the international literature. However, there is a lack of longitudinal studies examining this association in relation to proximal context effects, particularly in adolescence. Such findings would shed light on potential age- and proximal context-related variations in the anxiety–IA association that could better inform IA prevention and intervention initiatives.
In this study, 648 adolescents, embedded in 34 classrooms, were assessed at the age of 16 and again at the age of 18 to examine the effect of anxiety on IA behaviors in relation to the average level of classroom extraversion. IA was assessed with the Internet Addiction Test (Young, 1998), anxiety with the relevant subscale of the Symptom Checklist 90 – Revised (Derogatis & Savitz, 1999) and classroom extraversion with the synonymous subscale of the Five Factor Questionnaire (Asendorpf & van Aken, 2003). A three-level hierarchical linear model was calculated.
The present findings demonstrated that: (a) higher levels of anxiety were significantly associated with higher IA behaviors, (b) the strength of this association did not vary over time (between 16 and 18 years old), and (c) however, it tended to weaken within classrooms higher in extraversion.
This study indicated that the contribution of individual IA risk factors might differently unfold within different contexts.
In August of 2021, China imposed severe restrictions on children’s online gaming time. We argue that such a policy may seem useful on the surface but does not reflect the current evidence concerning prevention of disordered gaming. Videogame play is normal for children worldwide, and like other leisure activities can lead to benefits for the majority and problems for a minority. Problematic or disordered play results from the interaction of multiple risk factors that are not addressed by draconian policy measures. Identifying these factors through stakeholder-engaged research and current evidence will be much more likely to succeed in preventing disordered gaming and promoting youth wellbeing.
To date, a number of studies have investigated the prevalence and correlates of addictive food consumption. However, these studies have mostly relied on models that comprised a narrow range of variables in often small and heterogenous samples. The purpose of the present study was to comprehensively examine the measurement aspects, the prevalence, and the psychological correlates of addictive eating among a largescale national sample of Turkish adults.
Participants (N = 24,380, 50% men, Mage = 31.79 years, age range = 18–81 years) completed a battery of tests including the Food Addiction Risk Questionnaire (FARQ), the Brief Symptom Inventory, the Toronto Alexithymia Scale, the Positive and Negative Affect Schedule, and the Experiences in Close Relationships-Revised.
According to analyses conducted, the FARQ had a uni-dimensional factor structure. Based on Item Response Theory (IRT) calculated cut-off scores, 2.3% of the participants were at risk of addictive eating patterns, whilst criteria varied in their discriminating ability. The correlates of addictive food consumption were being male, being younger, having lower education, presenting with higher alcohol use, psychiatric symptoms, alexithymia, positive/negative affect, and anxious attachment.
These results suggest that a minority of Turkish community are at risk for addictive food consumption and that adverse psychological states promote this problematic behavior.
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.
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.
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.
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.