An overview of gambling disorder: from treatment approaches to risk factors

Menchón, José M.; Mestre‐Bach, Gemma; Steward, Trevor; Fernández‐Aranda, Fernando; Jiménez‐Murcia, Susana

doi:10.12688/f1000research.12784.1

Cited by 50 publications

(28 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this profile, therefore, gambling could be a dysfunctional habit acquired and maintained over time as a mechanism to avoid problems and difficulties in emotion regulation. Thus, it could represent a group of patients more inclined to present poor response to treatment, as various studies have previously shown (39–41). As such, they may require treatment programs that incorporate techniques to improve these aspects, favoring a better response to therapy (8).…”

Section: Discussionmentioning

confidence: 92%

Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype?

et al. 2019

Self Cite

View full text Add to dashboard Cite

Background: Gambling disorder (GD) is a heterogeneous disorder which has clinical manifestations that vary according to variables in each individual. Considering the importance of the application of specific therapeutic interventions, it is essential to obtain clinical classifications based on differentiated phenotypes for patients diagnosed with GD. Objectives: To identify gambling profiles in a large clinical sample of n = 2,570 patients seeking treatment for GD. Methods: An agglomerative hierarchical clustering method defining a combination of the Schwarz Bayesian Information Criterion and log-likelihood was used, considering a large set of variables including sociodemographic, gambling, psychopathological, and personality measures as indicators. Results: Three-mutually-exclusive groups were obtained. Cluster 1 ( n = 908 participants, 35.5%), labeled as “high emotional distress,” included the oldest patients with the longest illness duration, the highest GD severity, and the most severe levels of psychopathology. Cluster 2 ( n = 1,555, 60.5%), labeled as “mild emotional distress,” included patients with the lowest levels of GD severity and the lowest levels of psychopathology. Cluster 3 ( n = 107, 4.2%), labeled as “moderate emotional distress,” included the youngest patients with the shortest illness duration, the highest level of education and moderate levels of psychopathology. Conclusion: In this study, the general psychopathological state obtained the highest importance for clustering.

show abstract

Section: Discussionmentioning

confidence: 92%

Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype?

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Actually, it seems that in the community, most individuals who have had gambling problems end up recovering without having totally abstained from gambling behavior, during the process [25]. Therefore, although abstinence is the most common therapeutic goal in treatment programs [67], it could be timely to explore other alternatives such as controlled gambling, in the context of personalized therapeutic approaches, discussing with the patient their own goals and objectives [14, 68]. However, focusing on T4 and T5, it is necessary to bear in mind that other associated factors may be personality traits such as lower reward dependence and low persistence.…”

Section: Discussionmentioning

confidence: 99%

Developmental trajectories of gambling severity after cognitive-behavioral therapy

et al. 2019

Self Cite

View full text Add to dashboard Cite

Aims:To estimate trajectories of the gambling disorder (GD) severity for 12 months following a manualized cognitive-behavior-therapy (CBT) program, and to identify the main variables associated with each trajectory.Methods:Latent Class Growth Analysis examined the longitudinal changes of n = 603 treatment-seeking patients with GD.Results:Five separate empirical trajectories were identified: T1 (n = 383, 63.5%) was characterized by the most highest baseline gambling severity levels and positive progress to recovery during the follow-up period; T2 (n = 154, 25.5%) featured participants with high baseline gambling severity and good progress to recovery; T3 (n = 30, 5.0%) was made up of patients with high gambling baseline severity and slow progress to recovery; T4 (n = 13, 2.2%) and T5 (n = 23, 3.8%) contained participants with high baseline gambling severity and moderate (T4) and poor (T5) progress in GD severity during the follow-up. Psychopathological state and personality traits discriminated between trajectories. Poor compliance with the therapy guidelines and the presence of relapses also differed between the trajectories.Conclusions:Our findings show that patients seeking treatment for GD are heterogeneous and that trends in progress following treatment can be identified considering sociodemographic features, psychopathological state and personality traits. These results could be useful in developing more efficient interventions for GD patients.

show abstract

“…The therapeutic program has been described elsewhere and consists of individualized outpatient cognitive-behavioral therapy for PG, aimed at achieving abstinence from gambling. Treatment was protocolized, and the main techniques used were psychoeducation, motivational interviewing, stimulus control, cognitive restructuring and relapse prevention ( 16 , 17 ).…”

Section: Methodsmentioning

confidence: 99%

Current Addiction in Youth: Online Sports Betting

et al. 2021

View full text Add to dashboard Cite

Background: Gambling landscape has changed in recent years with the emergence of online gambling (OG). Greater accessibility and availability of this betting modality can increase the risk of developing a gambling disorder (GD). Online sports betting (OSB) is currently the most common type of OG, but little is known about the clinical characteristics of OSB compared to slot-machine (SM) gamblers, the most common offline gambling disorder.Methods: This was a prospective study conducted between October 2005 and September 2019, and included outpatients diagnosed with GD seen in a Pathological Gambling and Behavioral Addictions referral unit. Only patients with OSB and SM disorders were included. The main objective was to assess the clinical profile of OSB compared to SM gamblers, and to define clinical predictors for developing OSB gambling disorder. Logistic regression was performed to determine the effects of variables on the likelihood of this disorder.Results: Among 1,186 patients attended in our Unit during the study period, 873 patients were included; 32 (3.7%) were OSB gamblers and 841 (96.3%) were SM gamblers. Overall, mean age was 45 ± 13 years and 94.3% were men. Compared to SM patients, OSB patients were younger (34.9 ± 9.5 vs. 45.3 ±13), more frequently single (43.8 vs. 20.6%) and had a university education level (43.8 vs. 4.5%); they were also more frequently non-smokers (18.7 vs. 66.7%) and had fewer psychiatric comorbidities (12.5 vs. 29.4%) than SM gamblers. GD duration before treatment initiation was shorter in OSB patients than in SM gamblers, most of them (81.3 vs. 42.4%) with ≤ 5 years of GD duration. OSB gamblers showed significant differences in weekly gambling expenditure, spending higher amounts than SM patients. Younger age (OR: 0.919; 95% CI: 0.874–0.966), university education level (OR: 10.658; 95% CI: 3.330–34.119), weekly expenditure >100€ (OR: 5.811; 95% CI:1.544–21.869), and being a non-smoker (OR:13.248; 95% CI:4.332–40.517) were associated with an increased likelihood of OSB gambling behavior.Conclusions: We identified different profiles for OSB and SM gamblers. Younger age, university education level, higher weekly expenditure, and non-smoking habit were associated with OSB compared to SM disorders. Prevention strategies should help young people become aware of the severe risks of OSB.

show abstract

An overview of gambling disorder: from treatment approaches to risk factors

Cited by 50 publications

References 88 publications

Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype?

Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype?

Developmental trajectories of gambling severity after cognitive-behavioral therapy

Current Addiction in Youth: Online Sports Betting

Contact Info

Product

Resources

About