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Enregistrement W4315433416 · doi:10.3389/fpsyt.2022.1127444

Editorial: Behavioral addictions: Emerging science

2023· editorial· en· W4315433416 sur OpenAlexaff
Andreas Chatzittofis, Hyoun S. Kim

Notice bibliographique

RevueFrontiers in Psychiatry · 2023
Typeeditorial
Langueen
DomainePsychology
ThématiqueSexuality, Behavior, and Technology
Établissements canadiensUniversity of OttawaToronto Metropolitan UniversityRoyal Ottawa Mental Health Centre
Organismes subventionnairesnon disponible
Mots-clésAddictionPsychiatryPsychologyMedicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0030,003
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,360
Écart entre enseignants0,344 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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