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Abstract
Background and aims
Due to its important role in both healthy groups and those with physical, mental and behavioral disorders, impulsivity is a widely researched construct. Among various self-report questionnaires of impulsivity, the Barratt Impulsiveness Scale is arguably the most frequently used measure. Despite its international use, inconsistencies in the suggested factor structure of its latest version, the BIS-11, have been observed repeatedly in different samples. The goal of the present study was therefore to test the factor structure of the BIS-11 in several samples.
Methods
Exploratory and confirmatory factor analyses were conducted on two representative samples of Hungarian adults (N = 2,457; N = 2,040) and a college sample (N = 765).
Results
Analyses did not confirm the original model of the measure in any of the samples. Based on explorative factor analyses, an alternative three-factor model (cognitive impulsivity; behavioral impulsivity; and impatience/restlessness) of the Barratt Impulsiveness Scale is suggested. The pattern of the associations between the three factors and aggression, exercise, smoking, alcohol use, and psychological distress supports the construct validity of this new model.
Discussion
The new measurement model of impulsivity was confirmed in two independent samples. However, it requires further cross-cultural validation to clarify the content of self-reported impulsivity in both clinical and nonclinical samples.
Abstract
Background and aims
The present longitudinal study examined the changes in problematic internet use (problematic smartphone use, problematic social media use, and problematic gaming) and changes in COVID-19-related psychological distress (fear of COVID-19 and worry concerning COVID-19) across three time-points (before the COVID-19 outbreak, during the initial stages of the COVID-19 outbreak, and during the COVID-19 outbreak recovery period).
Methods
A total of 504 Chinese schoolchildren completed measures concerning problematic internet use and psychological distress across three time-points. Latent class analysis (LCA) was used to classify participants into three groups of problematic internet use comprising Group 1 (lowest level), Group 2 (moderate level), and Group 3 (highest level).
Results
Statistical analyses showed that as problematic use of internet-related activities declined among Group 3 participants across the three time points, participants in Group 1 and Group 2 had increased problematic use of internet-related activities. Although there was no between-group difference in relation to worrying concerning COVID-19 infection, Groups 2 and 3 had significantly higher levels of fear of COVID-19 than Group 1 during the COVID-19 recovery period. Regression analysis showed that change in problematic internet use predicted fear of COVID-19 during the recovery period.
Conclusion
The varied levels of problematic internet use among schoolchildren reflect different changing trends of additive behaviors during COVID-19 outbreak and recovery periods.
Abstract
Background and aims
Changes in the nomenclature of addictions suggest a significant shift in the conceptualization of addictions, where non-substance related behaviors can also be classified as addictions. A large amount of data provides empirical evidence that there are overlaps of different types of addictive behaviors in etiology, phenomenology, and in the underlying psychological and biological mechanisms. Our aim was to investigate the co-occurrences of a wide range of substance use and behavioral addictions.
Methods
The present epidemiological analysis was carried out as part of the Psychological and Genetic Factors of the Addictive Behaviors (PGA) Study, where data were collected from 3,003 adolescents and young adults (42.6% males; mean age 21 years). Addictions to psychoactive substances and behaviors were rigorously assessed.
Results
Data is provided on lifetime occurrences of the assessed substance uses, their co-occurrences, the prevalence estimates of specific behavioral addictions, and co-occurrences of different substance use and potentially addictive behaviors. Associations were found between (i) smoking and problematic Internet use, exercising, eating disorders, and gambling (ii) alcohol consumption and problematic Internet use, problematic online gaming, gambling, and eating disorders, and (iii) cannabis use and problematic online gaming and gambling.
Conclusions
The results suggest a large overlap between the occurrence of these addictions and behaviors and underlies the importance of investigating the possible common psychological, genetic and neural pathways. These data further support concepts such as the Reward Deficiency Syndrome and the component model of addictions that propose a common phenomenological and etiological background of different addictive and related behaviors.
Abstract
Background and aims
Internet use has become an important part of daily living. However, for a minority it may become problematic. Moreover, problematic use of the Internet/smartphone (PUIS) has been associated with low physical activity. The present study investigated the temporal associations between three types of PUIS (i.e., problematic smartphone use [PSPU], problematic social media use [PSMU] and problematic gaming [PG]) and physical activity among Taiwanese university students.
Methods
A six-month longitudinal survey study comprising three time points for assessments was conducted. From the original 974 participants, a total of 452 completed all three waves of an online survey comprising the International Physical Activity Questionnaire Short Form (IPAQ-SF) assessing physical activity level, Smartphone Application-Based Addiction Scale (SABAS) assessing PSPU, Bergen Social Media Addiction Scale (BSMAS) assessing PSMU, and Internet Gaming Disorder Short Form (IGDS9-SF) assessing PG.
Results
The linear mixed effects model found positive temporal associations of PSMU and PG with physical activity level (PSMU: B = 85.88, SE = 26.24; P = 0.001; PG: B = 36.81, SE = 15.17; P = 0.02). PSPU was not associated with physical activity level (B = 40.54, SE = 22.99; P = 0.08). Additionally, the prevalence rates were 44.4% for at-risk/PSPU, 24.6% for at-risk/PSMU, and 12.3% for at-risk/PG.
Discussion and Conclusions
PSMU and PG unexpectedly demonstrated correlations with higher physical activity level. The nature of these relationships warrants additional investigation into the underlying mechanisms in order to promote healthy lifestyles among university students.
Background and aims
Despite many positive benefits, mobile phone use can be associated with harmful and detrimental behaviors. The aim of this study was twofold: to examine (a) cross-cultural patterns of perceived dependence on mobile phones in ten European countries, first, grouped in four different regions (North: Finland and UK; South: Spain and Italy; East: Hungary and Poland; West: France, Belgium, Germany, and Switzerland), and second by country, and (b) how socio-demographics, geographic differences, mobile phone usage patterns, and associated activities predicted this perceived dependence.
Methods
A sample of 2,775 young adults (aged 18–29 years) were recruited in different European Universities who participated in an online survey. Measures included socio-demographic variables, patterns of mobile phone use, and the dependence subscale of a short version of the Problematic Mobile Phone Use Questionnaire (PMPUQ; Billieux, Van der Linden, & Rochat, 2008).
Results
The young adults from the Northern and Southern regions reported the heaviest use of mobile phones, whereas perceived dependence was less prevalent in the Eastern region. However, the proportion of highly dependent mobile phone users was more elevated in Belgium, UK, and France. Regression analysis identified several risk factors for increased scores on the PMPUQ dependence subscale, namely using mobile phones daily, being female, engaging in social networking, playing video games, shopping and viewing TV shows through the Internet, chatting and messaging, and using mobile phones for downloading-related activities.
Discussion and conclusions
Self-reported dependence on mobile phone use is influenced by frequency and specific application usage.
Online gaming has greatly increased in popularity in recent years, and with this has come a multiplicity of problems due to excessive involvement in gaming. Gaming disorder, both online and offline, has been defined for the first time in the draft of 11th revision of the International Classification of Diseases (ICD-11). National surveys have shown prevalence rates of gaming disorder/addiction of 10%–15% among young people in several Asian countries and of 1%–10% in their counterparts in some Western countries. Several diseases related to excessive gaming are now recognized, and clinics are being established to respond to individual, family, and community concerns, but many cases remain hidden. Gaming disorder shares many features with addictions due to psychoactive substances and with gambling disorder, and functional neuroimaging shows that similar areas of the brain are activated. Governments and health agencies worldwide are seeking for the effects of online gaming to be addressed, and for preventive approaches to be developed. Central to this effort is a need to delineate the nature of the problem, which is the purpose of the definitions in the draft of ICD-11.
Including gaming disorder in the ICD-11: The need to do so from a clinical and public health perspective
Commentary on: A weak scientific basis for gaming disorder: Let us err on the side of caution (van Rooij et al., 2018)
The proposed introduction of gaming disorder (GD) in the 11th revision of the International Classification of Diseases (ICD-11) developed by the World Health Organization (WHO) has led to a lively debate over the past year. Besides the broad support for the decision in the academic press, a recent publication by van Rooij et al. (2018) repeated the criticism raised against the inclusion of GD in ICD-11 by Aarseth et al. (2017). We argue that this group of researchers fails to recognize the clinical and public health considerations, which support the WHO perspective. It is important to recognize a range of biases that may influence this debate; in particular, the gaming industry may wish to diminish its responsibility by claiming that GD is not a public health problem, a position which maybe supported by arguments from scholars based in media psychology, computer games research, communication science, and related disciplines. However, just as with any other disease or disorder in the ICD-11, the decision whether or not to include GD is based on clinical evidence and public health needs. Therefore, we reiterate our conclusion that including GD reflects the essence of the ICD and will facilitate treatment and prevention for those who need it.
