A Randomized Trial of Personalized Cognitive-Behavior Therapy for Alcohol Use Disorder in a Public Health Clinic

From alcohol and other drug treatment mediator to mechanism to implementation: A systematic review and the cases of self-efficacy, social support, and craving

Research designed to establish alcohol and other drug (AOD) mechanisms of behavioral change (MOBC) has centered on what variables mediate the relation between AOD treatment and outcomes. The purpose of this paper was to review this research evidence to identify empirically supported mediators of alcohol and other drug use and related outcomes and then to evaluate their potential as being AOD treatment MOBC. The first phase was a systematic review of reviews (2008-2023) to identify the variables with the strongest empirical support as mediators of AOD treatment effects. Eligible reviews focused on AOD treatment modalities, included empirically tested mediators, and targeted adult samples. The second phase was a systematic review of empirical studies (1990-2023) testing the hypothesis that variables identified in phase one were AOD treatment mediators/mechanisms and then evaluating each eligible stage two study according to the Kazdin and Nock (Journal of Child Psychology and Psychiatry, 44, 1116) criteria. Eligible articles included empirical studies with adult samples attending AOD treatment and empirically tested one of the three treatment mechanisms as a mediator of an AOD-related outcome. Databases were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. This systematic review was not preregistered. The first review of 11 eligible review articles identified self-efficacy, social support, and craving as having the strongest empirical support. The second review captured 48 individual studies. An evaluation of each of these studies by the Kazdin and Nock criteria suggested that they likely are MOBC and therefore are ready for implementation. The implementation of self-efficacy, social support, and craving into clinical practice and training is warranted. Six directions for future research to solidify and generalize empirical support for the case that self-efficacy, social support, and craving are MOBC are presented, as are five implications for clinical practice and training.

Effectiveness of psychosocial interventions for alcohol use disorder: a systematic review and meta-analysis update

Background: Given the accumulating research, evolving psychosocial treatment, and equivocal findings, updating WHO's Mental Health Gap Action Programme-2015 was necessary to ensure guidelines reflect effective strategies for alcohol use disorder (AUD).Objective: To estimate the effects of psychosocial interventions on drinking and related outcomes.Methods: We included randomized controlled trials published between January 2015 and June 2022 on adults with alcohol dependence (ICD 10/DSM-IV) and moderate to severe AUD (DSM-5), and those examined psychosocial interventions against treatment-as-usual (TAU) and active controls. Eight databases and registries were searched. Relative Risk (RR) and standardized mean difference (SMD) were used for dichotomous and continuous outcomes. We used Cochrane's risk of bias assessment (RoB2).Results: Of 873 screened records, 14 and 13 studies in the narrative synthesis and meta-analysis. Of the 2,575 participants, 71.5% were men. Thirteen studies used ICD 10/DSM IV diagnosis. Compared to TAU, any psychosocial intervention increased the relative risk of abstinence by 28% [N = 7, RR = 1.28, 95% CI: 1.07 to 1.53, p = .01, NNT = 9]. There were minimal heterogeneity and no evidence of publication bias. Psychosocial interventions were not effective in reducing the drinking frequency (n = 2, Hedge's g = -0.10, 95% CI: -0.46 to 0.26, p = .57) and drinks/drinking days (N = 5, g = -0.10, 95% CI: -0.37 to 0.16, p = .43). Treatment discontinuation did not differ between intervention and control groups [RR = 1.09, 95% CI: 0.66 to 1.80].Conclusion: Psychosocial interventions are effective in improving abstinence but not in reducing drinking frequency or amount. Policymakers must consider this evidence to generate AUD treatment guidelines.Registration: PROSPERO 2022 CRD42022342608.

Physical activity: A promising adjunctive treatment for severe alcohol use disorder

Substance use disorder develops from complex interactions between socio-environmental and neurobiological factors. A neurocognitive model of addiction, the triadic model, proposes that Alcohol Use Disorder (AUD) is the result of an imbalance between the reflective and the impulsive subcomponents along with a disruption of the regulatory subcomponent. Physical activity is considered as an emerging treatment for severe AUD (sAUD). This short review examines the efficacy and mechanisms of action of physical intervention as an adjunctive treatment in severe AUD (sAUD) within the theoretical framework of the triadic model. Physical activity is a feasible, safe, and less stigmatizing approach than classical treatments. It improves sAUD patients' mental and physical comorbidities. The key finding of this short review is that physical activity could contribute to a rebalancing of the triadic model in sAUD patients by 1) improving neuroplasticity and cognitive functioning, 2) reducing impulsivity and urgency, and improving emotional regulation, and 3) reducing craving. This rebalancing could eventually reduce the risk of relapse. However, due to methodological issues, it remains difficult to observe an effect of physical activity on drinking outcomes. At best, a trend towards a reduction in alcohol consumption was noted. The mechanisms that could explain the benefits of physical activity in sAUD patients involve multiple physiological processes such as dopaminergic or glutamatergic transmission and signaling or neuroplasticity. Future randomized controlled trials should include neuropsychological and impulsivity assessments, in more controlled environments. Physical activity could contribute to a personalization of sAUD treatment using each subcomponent of the triadic model as a therapeutic target. Physical exercise could be an adjunctive treatment for sAUD patients, favoring the benefit of more usual treatments such as cognitive behavioral therapies. It could also be a stand-alone intervention in less severe patients.

Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus 'trained' machine learning models

BACKGROUND AND AIMS

Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML algorithms demonstrated to be effective in predicting alcohol dependence outcomes, compared with clinical judgement and traditional linear regression.

DESIGN

Prospective study. ML models were trained on 1016 previously treated patients (training-set) who attended a hospital-based alcohol and drug clinic. ML models (n = 27), clinical psychologists (n = 10) and a 'traditional' logistic regression model (n = 1) predicted treatment outcome during the initial treatment session of an alcohol dependence programme.

SETTING

A 12-week cognitive behavioural therapy (CBT)-based abstinence programme for alcohol dependence in a hospital-based alcohol and drug clinic in Australia.

PARTICIPANTS

Prospective predictions were made for 220 new patients (test-set; 70.91% male, mean age = 35.78 years, standard deviation = 9.19). Sixty-nine (31.36%) patients successfully completed treatment.

MEASUREMENTS

Treatment success was the primary outcome variable. The cross-validated training-set accuracy of ML models was used to determine optimal parameters for selecting models for prospective prediction. Accuracy, sensitivity, specificity, area under the receiver operator curve (AUC), Brier score and calibration curves were calculated and compared across predictions.

FINDINGS

The mean aggregate accuracy of the ML models (63.06%) was higher than the mean accuracy of psychologist predictions (56.36%). The most accurate ML model achieved 70% accuracy, as did logistic regression. Both were more accurate than psychologists (P < 0.05) and had superior calibration. The high specificity for the selected ML (79%) and logistic regression (90%) meant they were significantly (P < 0.001) more effective than psychologists (50%) at correctly identifying patients whose treatment was unsuccessful. For ML and logistic regression, high specificity came at the expense of sensitivity (26 and 31%, respectively), resulting in poor prediction of successful patients.

CONCLUSIONS

Machine learning models and logistic regression appear to be more accurate than psychologists at predicting treatment outcomes in an abstinence programme for alcohol dependence, but sensitivity is low.

Craving mediates the effect of impulsivity on lapse-risk during alcohol use disorder treatment

Rash impulsiveness, the propensity for approach behaviour despite potential negative consequences, is associated with stronger alcohol craving in patients with Alcohol Use Disorder (AUD). This relationship is poorly understood and implications for treatment response are unexamined. This study explored the relationship between rash impulsiveness, craving, and treatment response among 304 outpatients enrolled in a 12-week abstinence-based Cognitive-Behavioural Therapy (CBT) program for AUD. Assessments were completed pre-and-post treatment, with craving and alcohol consumption monitored at each treatment session. Higher rash impulsiveness predicted more frequent craving over treatment (b = 0.95, 95% CI = 0.40, 1.50). Higher craving was associated with greater lapse-risk (b = 0.04, 95% CI = 0.03, 0.05), with the association between craving and lapse-risk increasing as treatment progressed (b = 0.01, 95% CI = 0.01, 0.02). Craving positively mediated the relationship between rash impulsiveness and lapse-risk (µ = 0.38, 95% CI = 0.10, 0.70). Contrary to hypotheses, the risk of lapse in response to craving was not moderated by rash-impulsiveness. These results suggest that AUD patients with a predisposition for rash impulsiveness are more vulnerable to alcohol craving, and subsequently, poorer treatment outcomes.

Gender variations in citation distributions in medicine are very small and due to self-citation and journal prestige

A number of studies suggest that scientific papers with women in leading-author positions attract fewer citations than those with men in leading-author positions. We report the results of a matched case-control study of 1,269,542 papers in selected areas of medicine published between 2008 and 2014. We find that papers with female authors are, on average, cited between 6.5 and 12.6% less than papers with male authors. However, the standardized mean differences are very small, and the percentage overlaps between the distributions for male and female authors are extensive. Adjusting for self-citations, number of authors, international collaboration and journal prestige, we find near-identical per-paper citation impact for women and men in first and last author positions, with self-citations and journal prestige accounting for most of the small average differences. Our study demonstrates the importance of focusing greater attention on within-group variability and between-group overlap of distributions when interpreting and reporting results of gender-based comparisons of citation impact.

Testing the biosocial cognitive model of substance use in cannabis users referred to treatment

BACKGROUND

The bioSocial Cognitive Theory (bSCT) hypothesizes two pathways linking dimensions of impulsivity to substance use. The first predicts that the association between reward sensitivity and substance use is mediated by positive outcome expectancies. The second predicts that the relationship between rash impulsiveness and substance use is mediated by refusal self-efficacy. This model has received empirical support in studies of alcohol use. The present research provides the first application of bSCT to a cannabis treatment population and aims to extend its utility to understanding cannabis use and severity of dependence.

DESIGN

273 patients referred for cannabis treatment completed a clinical assessment that contained measures of interest.

SETTING

A public hospital alcohol and drug clinic.

MEASUREMENTS

The Sensitivity to Reward Scale, Dysfunctional Impulsivity Scale, Cannabis Expectancy Questionnaire, Cannabis Refusal Self-Efficacy Questionnaire and Severity of Dependence Scale-Cannabis were completed, along with measures of cannabis consumption.

FINDINGS

The bSCT model provided a good fit to the data for cannabis use and severity of dependence outcomes. The association between reward sensitivity and each cannabis outcome was fully mediated by positive cannabis expectancies and cannabis refusal self-efficacy. The relationship between rash impulsiveness and each cannabis outcome was fully mediated by cannabis refusal self-efficacy.

CONCLUSIONS

Findings support the application of the bSCT model to cannabis use and dependence severity and highlight the important role of social cognitive mechanisms in understanding the association between impulsivity traits and these outcomes. The differential association of impulsivity traits to social cognition may assist targeted treatment efforts.