Editorial: Behavioral addictions: Emerging science
Bibliographic record
Abstract
In recent years, besides psychoactive substances, certain maladaptive behaviors have also been considered to be in the spectrum of addiction and classified as non-substance or behavioral addictions (1,2). These behavioural addictions include those recognized in the International Statistical Classification of Diseases and Related health problems (11th ed.) (ICD-11) and the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) such as gambling disorder and gaming disorder as well as putative behavioral addictions such as patterns of addictive internet use, problematic smartphone use, and shopping/buying disorder, while compulsive sexual behaviour disorder is under "Impulse control disorder" (3,4).Although for some of these disorders there is preliminary data on the underlying mechanism, the pathophysiology is far from clear (5)(6)(7). As the debate on the exact phenomenology and classification of these mental health disorders is ongoing, there is an urgent need for further research in the field. In addition, the comorbidity of behavioral addictions with other psychiatric disorders is of importance as it raises new challenges for both the assessment and treatment of patients. The COVID-19 pandemic and the quarantine measures applied also had an effect on behavioral addictions with reported increases in the use of the internet, pornography, and gaming (8). This Research Topic aims to gather new empirical data and highlight recent advances in behavioral addictions with a focus on comorbidity with other psychiatric disorders as well as the impact of the COVID-19 pandemic on behavioral addictions. We are excited to present the following articles, composing this Research Topic adding new elements to the understanding of these complex disorders, providing insight in their recognition and management.Blinka et al conducted a qualitative study of 23 men in treatment for problematic internet sex use focusing on the phenomenology of psychiatric symptoms. Common patterns were pornography use and cybersex, with continuous masturbation on a daily basis starting in early adulthood and continuing through the years. The symptoms were consisted with the addiction model with loss of control and preoccupation being the most profound symptoms. Together with the onset of erectile dysfunction, negative consequences developed slowly and included life dissatisfaction, regret and feelings of unfulfilled potential.In a study of 325 healthy adults, Guo et al applying network analysis, reported that the dimensions of impulsivity were closely associated with the components social media addiction and problematic smartphone use. The authors revealed that "motor impulsivity" was the most critical bridge node in both networks and propose it as a promising target for applying preventive and treatment interventions for social media addiction and problematic smartphone use. Regarding comorbidity, in a study by Machado et al, the authors investigated gender differences in adults seeking treatment for problematic internet use. Women had more psychiatric comorbidities compared to men and more severe behavioral addictions, such as compulsive buying and disordered eating. These women had also higher scores in impulsivity, novelty seeking, and self-transcendence compared to men. These results highlight the importance of assessing for co-occurring conditions in this clinical population.Zhu et al conducted a case-control study among 84 adolescents with adolescent nonsuicidal self-injury to characterize the behavior addiction characteristics of the group.Factors such as being female, being only child, presence of internet addiction, and negative parenting styles were predictors of NSSI behavioral addiction characteristics in adolescents. Thus, the authors suggest that the development of coping strategies targeting this vulnerable group.Regarding the effects of the COVID 19 pandemic, Otis et al investigated the gambling behavior of 85 sports gamblers during the course of the pandemic. The hypothesis on an initial decline in the early stages of the pandemic (due to availability restrictions), followed by an increase in gambling behaviours the months after the restart of live sporting events was partly supported, although gambling behaviors did not completely return to baseline levels. These results may have implications regarding legislation concerning access to gambling.An extensive systematic review on the efficacy and tolerability of therapeutic interventions (psychological and pharmacological), for buying/shopping disorder is published by Vasiliu concluding that cognitive behavioral therapy (CBT) is supported by the current evidence, followed by the combination of CBT + antidepressants as well as monotherapy with serotoninergic antidepressants. This review helps clinicians to choose the most evidence-based treatment for these patients and emphasizes the need for high-quality trials.In conclusion, recognizing the limitations of current knowledge, we should emphasize the need for further research in the field of behavioural addictions. Focus should be on the underlying mechanisms of these disorders elucidating both the phenomenology and pathophysiology helping the better classification and our understanding. Finally, research is urgently needed on applied interventions and management of behavioural addictions in order to minimize their burden on the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".