Introduction: Contingency management (CM) is currently recommended for cocaine use disorder by international scientific collaborations and public health institutions. This study addresses the gap between the established efficacy of CM and its implementation in routine clinical practice, by examining the acceptance and course of CM as a stand-alone intervention in patients with cocaine use disorder. Methods: Twenty-nine cocaine-dependent outpatients participated in a naturalistic cohort study and received a 12-week CM intervention. The primary outcome measures were treatment acceptance and treatment retention. Secondary outcomes included cocaine and other substance use, physical and mental health, quality of life, and impulsivity. Results: Of the 355 patients assessed for eligibility, 57 were offered CM and 29 initiated treatment, resulting in an acceptance rate of 50.9%. Participants attended CM for an average of 6.8 weeks and submitted 42% of scheduled urine samples. Among the 17 participants who submitted at least one cocaine-negative urine sample, eight were classified as having a relatively higher level of abstinence based on the median number of cocaine-negative samples (≥15). In this exploratory subgroup, cocaine use decreased significantly from 13.8 days in the four weeks preceding treatment to 0.6 days in the final four weeks of the intervention, alongside significant reductions in depressive and anxiety symptoms. No serious adverse events occurred. Conclusions: Despite the small sample size and high dropout rate during CM treatment, this study provides a comprehensive overview of the implementation, progression, and acceptance of CM in a complex patient population. Further research is needed to identify factors influencing treatment acceptance and retention, as well as clinical outcomes and predictors of effective participation in CM.
Introduction: Online–offline gambling comparisons rarely distinguish debt occurrence from debt amount. Problem-stage gambling modality and recalled modality pathways were examined in relation to symptom severity and gambling-related debt. Methods: This retrospective cross-sectional study analyzed 2019–2024 intake records from South Korea’s national gambling counseling service. The sample comprised 567 help-seeking individuals meeting DSM-5 criteria for gambling disorder. Regression models examined DSM-5 criterion count, Problem Gambling Severity Index (PGSI) score, any debt, and debt amount among those with debt. Models adjusted for age, sex, assessment year, product group, and legal status. Additional analyses examined modality pathways, recalled age at first gambling, and age-related differences. Results: At the problem stage, 89.8% reported online gambling. Among 553 participants in three modeled pathways, 9.4% were persistent offline, 14.1% migrated online, and 76.5% were persistent online. Fully adjusted models showed no clear online–offline differences in DSM-5 criterion count, PGSI score, or any debt. Among 242 complete cases with debt, adjusted mean debt was twice as high online as offline (ratio 2.00, 95% CI 1.11–3.62). Persistent-online participants also had higher debt than persistent-offline participants (ratio 2.56, 95% CI 1.32–4.95). Online gambling was associated with greater recent gambling intensity. Persistent-online participants reported a younger recalled age at first gambling. An exploratory interaction suggested a larger online–offline debt ratio at older ages. Conclusion: Online gambling was associated with larger debt among participants with debt, despite no clear differences in symptom severity or debt occurrence. Clinical assessment should record debt amount alongside symptom severity and modality history.
Methods. Participants were 1853 young adults (M = 22.4, SD = 2.10). They completed the Smartphone Application-Based Addiction Scale (SABAS), Bergen Social Media Addiction Scale (BSMAS), Multidimensional Digital Stress Scale (DSS), Digital Wellbeing Questionnaire (DWBQ), Perceived Digital Wellbeing Scale (PDWS), and Digital Flourishing Scale (DFS). Wellbeing was assessed with the Warwick–Edinburgh Mental Wellbeing Scale (SWEMWBS), Satisfaction With Life Scale (SWLS), a World Values Survey happiness item, and the life satisfaction ladder. Standardized scores were analyzed with k-means clustering; validity was tested with MANOVA and logistic regression. Results. Two clusters were identified. People in a balanced use cluster (n = 893) showed lower smartphone and social media addiction and digital stress, and higher flourishing, DWBQ, and PDWS scores. People with a dysregulated use cluster (n = 960) showed the opposite. MANOVA confirmed wellbeing differences, Wilks’ λ = 0.926, F(4, 1848) = 36.74, p < .001, partial η² = 0.074. People in a balanced use cluster scored higher on mental wellbeing (η² = 0.069), life satisfaction (η² = 0.024), happiness (η² = 0.033), and the ladder (η² = 0.006). Logistic regression showed gender, sexual orientation, and relationship status predicted membership. Discussion. Two profiles of digital engagement emerged. The PDWS distinguished clusters more strongly than the DWBQ, suggesting perceived balance is crucial for identifying risk and resilience. Interventions should focus on reducing digital stress and promoting flourishing rather than limiting screen time.
Introduction: Psychiatric disorders are common among patients with opioid use disorder (OUD). Opioid agonist therapy (OAT) is an effective treatment for OUD, but outcomes for patients with psychiatric comorbidities remain understudied. This study aimed to examine the connections between psychiatric comorbidities and outcomes in individuals undergoing OAT. Methods: We retrospectively collected data from the Wellbeing Services County of Southwest Finland clinical registry. The cohort included 1,235 patients receiving OAT from January 1, 2004, to April 15, 2021. Patients attending OAT, psychiatric diagnoses, overdoses, and self-harm were identified from the registry data using ICD-10 codes. In cases of death, the register also included the date of death. We analyzed associations between psychiatric comorbidities and outcomes using Cox regression for death and logistic regression for nonfatal overdose and self-harm. Results: We identified the prevalence of the following psychiatric comorbidities: non-substance use disorder (non-SUD) psychiatric disorders (39.0%), psychosis (17.0%), mood disorders (29.1%), anxiety disorders (26.2%), personality disorders (20.7%), and other SUDs not related to opioids (other SUDs) (61.4%). After adjusting for all covariates, we found an association between alcohol use disorder (AUD) (hazard ratio = 1.54, 95% confidence interval = 1.06–2.22) and a higher risk of death during OAT. Additionally, non-SUD psychiatric disorders (overdose: odds ratio [OR] = 1.64, 1.14–2.36; self-harm: OR = 1.50, 1.03–2.18), psychosis (overdose: OR = 1.89, 1.29–2.79; self-harm: OR = 1.87, 1.26–2.78), and other SUDs (overdose: OR = 2.61, 1.81–3.83; self-harm: OR = 2.56, 1.74–3.82) were associated with higher odds of both overdose and self-harm. Conclusion: The study emphasizes the need for screening and secondary preventive measures for psychosis and other SUDs (particularly AUD) in OAT settings.
Introduction: Despite widespread cannabis use, longitudinal evidence on quality-of-life impacts remains limited, particularly in regulated access contexts with objective purchase tracking. Methods: We conducted an 18-month longitudinal analysis of 300 participants in the [ANONYMIZED] study, a regulated cannabis access study in Switzerland. Quality of life was assessed using the EQ-5D at baseline and 18 months. Cannabis consumption was tracked objectively through pharmacy-based purchases, with additional measures of tetrahydrocannabinol (THC) content, self-reported problematic cannabis use (CUDIT-R), and self-reported consumption amount. Multiple regression analyses examined predictors of quality-of-life outcomes. Results: Overall quality of life remained stable from baseline (M=0.92, SD=0.18) to 18-month follow-up (mean change = 0.002, p=0.418). However, specific consumption patterns significantly predicted outcomes. Higher cumulative THC exposure over 18 months (b=-0.003, p=0.048), higher baseline CUDIT-R scores (b=-0.001, p=0.030), and increased self-reported consumption amount (b=-0.016, p=0.004) all independently predicted lower quality of life at follow-up. Notably, total cannabis quantity purchased through regulated channels did not predict outcomes (p=0.134). Conclusions: While regulated cannabis access does not lead to overall quality of life deterioration, THC exposure and consumption intensity—rather than absolute cannabis quantity purchased—are key determinants of wellbeing outcomes. These findings have important implications for harm reduction strategies and regulatory frameworks, suggesting that cannabis potency and use patterns matter more than total consumption amounts for long-term quality of life.
