Development of the motivational thought frequency scale for alcohol abstinence (MTF-A).

BACKGROUND AND AIMS: For most treatment-seeking patients with severe Alcohol Use Disorder (AUD), abstinence is the clinically indicated goal. Existing AUD motivation scales are non-specific about treatment consumption goals, which limit their effectiveness. Desires and mental imagery are relevant in the motivation for AUD treatment engagement. The Motivational Thought Frequency Scale for an abstinence goal (MTF-A) was adapted from the MTF for controlled drinking (MTF-CD). This study psychometrically evaluated the MTF-A in an alcohol-dependent sample engaged in treatment with a goal of abstinence. To enhance the clinical utility of the scale, a secondary aim was to evaluate a psychometrically equivalent short version of the MTF-A. METHOD: A sample N of 329 treatment-seeking patients with AUD (mean age of 44.44 years, SD = 11.89 years, 72% male) who were undertaking a cognitive behavioral treatment (CBT) program for abstinence completed the Motivational Thought Frequency Scale for Abstinence (MTF-A) and the Severity of Alcohol Dependence Questionnaire (SADQ). The MTF-A measured motivation for abstinence through four factors: intensity, self-efficacy imagery, incentives imagery, and availability. Confirmatory factor analyses (CFAs) were conducted to examine factor structure and model fit. Cronbach's alpha assessed internal consistency. Predictive validity was determined by logistic regression predicting first-session treatment non-attendance and alcohol consumption between baseline assessment and commencement of treatment, controlling for potential confounds. RESULTS: A four-factor structure provided the best fit for the MTF-A, compared with one- and three-factor models. A shortened 9-item MTF-A scale (S-MTF-A) provided better fit than the 13-item MTF-A scale. Both MTF-A and S-MTF-A displayed good internal consistency. Although both MTF-A and S-MTF-A successfully predicted first-session treatment non-attendance, neither predicted alcohol consumption between the baseline assessment and commencement of treatment. CONCLUSIONS: The model fit of the four-factor, 9-item S-MTF-A was superior to the original 13-item MTF-A. Both scales were predictive of participation of AUD treatment. Desires and mental imagery play an important role in AUD treatment motivation.

Relationship between alcohol craving dimensions and features of comorbid mental health in an alcohol dependent sample.

BACKGROUND AND AIMS: Negative affect and alcohol craving are common features of Alcohol Use Disorder (AUD). Both independently contribute to AUD severity and poorer treatment outcomes, but their relationship is poorly understood. Multidimensional alcohol craving measures now allow for examination of key dimensions of craving. This study explored the relationship between depression, anxiety, stress, and the alcohol craving dimensions of intensity, imagery and intrusiveness. METHOD: Five-hundred and twenty-five treatment seeking AUD patients (mean age of 39.79 years, SD = 11.57 years, 67% male) completed the Depression Anxiety Stress Scales (DASS), Alcohol Use Disorder Identification Test-Consumption items (AUDIT-C), and Alcohol Craving Experience (ACE-F) questionnaire, which measured the frequency of craving intensity, imagery and intrusiveness. Regression models predicted main effects of predictors and moderation by alcohol consumption. RESULTS: Higher levels of stress were independently associated with increased craving intensity, imagery and intrusiveness. Significant positive associations were also found between anxiety and craving imagery. The association between depression and craving was not significant after controlling for other predictors. CONCLUSIONS: AUD patients experienced higher cravings when stressed and greater imagery when anxious. These results support the need to consider the relationships between stress and craving when managing alcohol dependence.

Development and validation of the Stimulant Refusal Self-Efficacy Questionnaire (SRSEQ) in stimulant users in treatment.

BACKGROUND: Self-efficacy is a core component of Social Cognitive Theory. Refusal self-efficacy is an individual's belief in their ability to refuse a substance in specific high-risk situations. Change in refusal self-efficacy is predictive of positive treatment outcomes. Measurement of refusal self-efficacy is critical as it directs coping skills development techniques though existing behavioural treatments. There is no validated measure of stimulant refusal self-efficacy. This study developed and validated the Stimulant Refusal Self-Efficacy Questionnaire (SRSEQ) to measure confidence in the ability to refuse stimulants in specific high-risk situations. METHOD: Two hundred and seven stimulant-using patients referred for assessment completed the SRSEQ and measures including stimulant dependence severity (Severity of Dependence Scale-Stimulant, SDS-S). Confirmatory Factor Analysis (CFA) using structural equation modelling (SEM) was conducted to test the theoretically-driven three-factor structure of the SRSEQ. Criterion validity was tested with severity of stimulant dependence. RESULTS: The CFA supported the three-factor structure. Emotional Relief (ß = -0.27, p = .008), Opportunistic (ß = -0.24, p = .013) and Social Facilitation (ß = -0.32, p < .001) refusal self-efficacy were uniquely associated with stimulant dependence severity, explaining 55.1% variance. CONCLUSIONS: The SRSEQ is psychometrically sound and may be clinically useful to assist with assessment and treatment planning for stimulant use disorder.

Precision Mental Health Care for Cannabis Use Disorder: Utility of a bioSocial Cognitive Theory to Inform Treatment.

Globally, cannabis is the most frequently used controlled substance after alcohol and tobacco. Rates of cannabis use are steadily increasing in many countries and there is emerging evidence that there is likely to be greater risk due to increased concentrations of delta-9-tetrahydrocannabinol (THC). Cannabis use and Cannabis Use Disorder (CUD) has been linked to a wide range of adverse health outcomes. Several biological, psychological, and social risk factors are potential targets for effective evidence-based treatments for CUD. There are no effective medications for CUD and psychological interventions are the main form of treatment. Psychological treatments based on Social Cognitive Theory (SCT) emphasize the importance of targeting 2 keys psychological mechanisms: drug outcome expectancies and low drug refusal self-efficacy. This mini-review summarizes the evidence on the role of these mechanisms in the initiation, maintenance, and cessation of cannabis use. It also reviews recent evidence showing how these psychological mechanisms are affected by social and biologically-based risk factors. A new bioSocial Cognitive Theory (bSCT) is outlined that integrates these findings and implications for psychological cannabis interventions are discussed. Preliminary evidence supports the application of bSCT to improve intervention outcomes through better targeted treatment.

Additive effectiveness and feasibility of a theory-driven instant assessment and feedback system in brief cannabis intervention: A randomised controlled trial.

Assessment and personalised feedback are important components of brief interventions (BIs) for cannabis use. A key outcome is to increase motivation to change during this short interaction. The diversity of available assessments and time burden scoring them pose a challenge for routine use in clinical practice. An instant assessment and feedback (iAx) system was developed to administer assessments informed by bioSocial Cognitive Theory, that were instantly scored and benchmarked against clinical norms, to provide patient feedback and guide treatment planning. This study evaluated the feasibility and additive effectiveness of the iAx on motivation to change cannabis use, when compared to treatment as usual (TAU), in a single-session BI. A randomised controlled trial was conducted in a public hospital alcohol and drug outpatient clinic. Eighty-seven cannabis users (Mage = 26.41; 66% male) were assigned to the BI utilising the iAx (iAx; n = 44) or to the standard BI (TAU; n = 43). Patients completed pre- and post-BI assessments of motivation to change and a post-BI measure of treatment satisfaction. Practitioners completed a feedback survey. Patients receiving iAx reported a significantly greater increase in motivation to change from pre- to post-BI compared to patients receiving TAU (d = 0.49, p = .03). Treatment satisfaction was high across both conditions, with no significant difference between groups (p = .57). Practitioners also reported a high level of satisfaction with the iAx system. In summary, findings support the feasibility and additive effectiveness of the iAx to enhance patient motivation during cannabis BI.

The Influence of Alexithymia on Alcohol Craving, Health-Related Quality of Life and Gender in Alcohol-Dependent Outpatients.

Alexithymia is a vulnerability factor for physical and mental illness that can significantly influence the daily function of alcohol-dependent patients. The aim of this study was to examine the indirect effect of obsessive thoughts and compulsive behaviors involving alcohol craving on the relationship between alexithymia, quality of life (QoL) of psychological well-being and health status. Three hundred and eighty-one outpatients (263 males and 118 females) in treatment for alcohol dependence completed self-report measures of alexithymia, alcohol craving, GHQ-28 (QoL-psychological well-being) and SF-36 (QoL-health status). Males scored significantly higher than females on aspects of alexithymia, and females reported significantly higher levels of alcohol craving. Path analysis showed an indirect effect of alcohol craving on the relationship between alexithymia, QoL-psychological well-being and self-reported QoL-health status for males only. The current study provides important new information about impaired self-reported health status and well-being among male alcohol-dependent treatment seekers with alexithymia.

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 (µâ€¯= 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.

Alexithymia and Alcohol Dependence: The Roles of Negative Mood and Alcohol Craving.

Background: Alexithymia is a personality trait associated with emotion regulation difficulties. Up to 67% of alcohol-dependent patients in treatment have alexithymia. Objectives: The objective of this study was to investigate the direct and indirect effects of alexithymia, negative mood (stress, anxiety, and depression) and alcohol craving on alcohol dependence severity. Methods: Three hundred and fifty-five outpatients (mean age = 38.70, SD = 11.00, 244 males, range 18-71 years) undergoing Cognitive-Behavioral Therapy for alcohol dependence completed the Toronto Alexithymia Scale (TAS-20), Depression Anxiety Stress Scales (DASS-21), Obsessive Compulsive Drinking Scale (OCDS), and Alcohol Use Disorders Identification Test (AUDIT) prior to the first treatment session. Results: Alexithymia had an indirect effect on alcohol dependence severity, via both negative mood and alcohol craving (b = 0.03, seb = 0.008, 95% CI: 0.02-0.05). An indirect effect of negative mood on alcohol dependence via alcohol craving was also observed (b = 0.12, seb = 0.03, 95% CI: 0.07-0.16). Conclusions/importance: Alexithymia worked through negative mood and alcohol craving leading to increased alcohol dependence severity, indicating that craving had an indirect effect on the relationship between alexithymia and alcohol dependence severity. Targeting alcohol craving and negative mood for alcohol-dependent patients with alexithymia seems warranted.

Machine learning vs addiction therapists: A pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication.

BACKGROUND AND OBJECTIVES: Clinical staff providing addiction treatment predict patient outcome poorly. Prognoses based on linear statistics are rarely replicated. Addiction is a complex non-linear behavior. Incorporating non-linear models, Machine Learning (ML) has successfully predicted treatment outcome when applied in other areas of medicine. Using identical assessment data across the two groups, this study compares the accuracy of ML models versus clinical staff to predict alcohol dependence treatment outcome in behavior therapy using patient data only. METHODS: Machine learning models (n = 28) were constructed ('trained') using demographic and psychometric assessment data from 780 previously treated patients who had undertaken a 12 week, abstinence-based Cognitive Behavioral Therapy program for alcohol dependence. Independent predictions applying assessment data for an additional 50 consecutive patients were obtained from 10 experienced addiction therapists and the 28 trained ML models. The predictive accuracy of the ML models and the addiction therapists was then compared with further investigation of the 10 best models selected by cross-validated accuracy on the training-set. Variables selected as important for prediction by staff and the most accurate ML model were examined. RESULTS: The most accurate ML model (Fuzzy Unordered Rule Induction Algorithm, 74%) was significantly more accurate than the four least accurate clinical staff (51%-40%). However, the robustness of this finding may be limited by the moderate area under the receiver operator curve (AUC = 0.49). There was no significant difference in mean aggregate predictive accuracy between 10 clinical staff (56.1%) and the 28 best models (58.57%). Addiction therapists favoured demographic and consumption variables compared with the ML model using more questionnaire subscales. CONCLUSIONS: The majority of staff and ML models were not more accurate than suggested by chance. However, the best performing prediction models may provide useful adjunctive information to standard clinically available prognostic data to more effectively target treatment approaches in clinical settings.

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.

Short message service (SMS) reminders improve treatment attendance in alcohol dependence, but are less effective for patients high in impulsivity.

BACKGROUND AND AIMS: Poor attendance increases the likelihood of relapse in alcohol dependence treatment. Evidence for improved attendance rates following introduction of short message service (SMS) appointment reminders is available in other health care domains. Patients high in impulsivity, characterized by a lack of planning, may particularly benefit from reminders. The study investigated the impact of SMS reminders on outpatient treatment attendance for alcohol dependence, and whether effects were moderated by impulsivity. DESIGN: Prospective natural history study, with historical case control. Alcohol-dependent outpatients attending treatment received SMS appointment reminders (n = 102). These were compared to a historical control group (n = 91) treated prior to the introduction of SMS (totalling 1149 scheduled sessions). SETTING: A metropolitan university hospital alcohol and drug outpatient clinic. PARTICIPANTS: 193 alcohol-dependent patients participated in a 12-week cognitive-behavioral therapy (CBT) program with a treatment goal of abstinence. MEASUREMENTS: Trait impulsivity, severity of dependence, psychological distress at baseline. Attendance at each scheduled session. FINDINGS: SMS reminders significantly increased probability of session attendance (0.90 versus 0.84, p = .02). The effect was qualified by a significant SMS x Impulsivity interaction whereby reminders became less effective with increasing patient impulsivity (p = .003). CONCLUSIONS: SMS appointment reminders improve treatment attendance for alcohol-dependent outpatients. More impulsive patients benefited less from reminders, suggesting their non-attendance may be related more to motivational factors.

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

Background: Tailored psychological interventions based on individual risk factors are likely to improve treatment for Alcohol Use Disorders (AUDs). Key risk factors for poor treatment outcome include alcohol craving, positive expectations of alcohol consumption, and impulsivity. Design: Pragmatic randomized Cognitive-Behavioral Treatment (CBT) trial. Setting: Public hospital alcohol and drug clinic. Participants: Three-hundred seventy-nine patients (65% male; AgeyearsM = 44.32, SD = 10.75) seeking treatment for AUD. Procedure: Patients were randomly allocated into treatment as usual (TAU) or targeted treatment. Patients in targeted treatment were allocated one of three treatment modules focusing on craving, positive expectancy, or impulsivity based on assessment results. Treatment included eight, 1 h sessions of CBT over 12 weeks delivered by clinical psychologists. Hypotheses: Targeted treatment was expected to have fewer drinking days and consume less alcohol during the treatment period than TAU. Improvement in targeted mechanisms was predicted to be greatest for patients within matched conditions. Results: Patients attended an average of 4.4 sessions with 93 (25%) completing the whole 12-week treatment episode. The mean proportion of drinking days between sessions was 5% with an average consumption of 64 grams of ethanol. No significant effect of targeted treatment was identified on drinking days or consumption. The craving (b = -18.97, 95% CI = -31.44, -6.51) and impulsivity (b = -26.65, 95% CI = -42.09, -11.22) modules demonstrated significant reductions in their targeted constructs over treatment, above TAU. Only reduction in craving was associated with reduced drinking days [exp(b) = 0.958, p = 0.003] and alcohol consumption [exp(b) = 0.962, p = 0.02]. Significant indirect effects for the targeted craving module through craving reduction were identified for reduction in drinking days (ß = -0.72, 95% CI = -1.50, -0.158) and alcohol consumption (ß = -0.78, 95% CI = -1.72, -0.11). Conclusions: In the context of a public health service, the effectiveness of individualized treatment targeting risk mechanisms identified during pre-treatment assessment was not confirmed. Some evidence was found for improved treatment response to the implementation of a manualized craving module when pre-treatment craving was high.

Alcohol expectancies pre-and post-alcohol use disorder treatment: Clinical implications.

BACKGROUND AND AIMS: Modification of elevated positive expectations of alcohol consumption (alcohol outcome expectancies; AOEs) is a key feature of Cognitive Behaviour Therapy (CBT) approaches to Alcohol Use Disorders (AUDs). Despite extensive research supporting the efficacy of CBT for AUD, few studies have examined AOE change. This study aimed to assess AOE change following completion of CBT for AUD and its association with drinking behaviour. METHOD: One-hundred and seventy-five patients who completed a 12-week CBT program for AUD were administered the Drinking Expectancy Questionnaire (DEQ) at pre-treatment assessment and upon completion of treatment. Abstinence was achieved by 108 (61.7%) of completing patients. For patients who lapsed, the mean proportion of abstinent days was 93%. RESULTS: DEQ scales assessing expectations of positive alcohol effects on tension reduction, assertiveness, and cognitive enhancement were significantly lower post-treatment (p<0.001). Expectations of negative effects on mood were higher post-treatment (p<0.001). The largest AOE change occurred on the tension reduction scale. Greater percentage of abstinent days over treatment was associated with lower pre-and post-treatment tension reduction expectancy scores (p<0.05). Drinking during treatment was associated with smaller changes in expectations of negative effects of alcohol on mood (p<0.05). CONCLUSIONS: Individuals who completed CBT treatment for AUD showed significant AOE change. Tension reduction and affective change expectancies may be particularly important for abstinence and useful markers of lapse risk.

The Mini Alcohol Craving Experience Questionnaire: Development and Clinical Application.

BACKGROUND: Standardized alcohol craving scales are rarely used outside of research environments despite recognized clinical utility. Scale length is a key barrier to more widespread application. A brief measure of alcohol craving is needed to improve research and treatment of alcohol use disorders (AUDs). Grounded in the Elaborated Intrusion Theory of Desire, the Alcohol Craving Experience (ACE) Questionnaire comprises two 11-item self-report scales that assess past-week frequency and maximum strength of alcohol craving. This study aimed to create a brief version of the ACE while maintaining psychometric integrity and clinical utility. METHODS: Patients attending a university hospital alcohol and drug outpatient service for the treatment of AUD completed the ACE as part of a questionnaire battery. Three patient samples were utilized: 519 patients with pretreatment and outcome data, 228 patients with pretreatment data, and 66 patients who completed the ACE at treatment sessions 1 and 2. RESULTS: The Frequency scale of the ACE possessed greater clinical utility and predictive validity than the Strength scale. Revision of the Frequency measure produced a 5-item "Mini Alcohol Craving Experience" (MACE) Questionnaire. Satisfactory validity (construct, predictive, concurrent, convergent, and incremental) and reliability (internal and test-retest) were maintained. A 1 standard deviation increase in pretreatment MACE score was associated with a 54 percentage increase in the odds of patient lapse or dropout. CONCLUSIONS: The MACE provides a brief, theoretically, and psychometrically robust measure of alcohol craving suitable for use with AUD populations in time-limited clinical and research settings.

Social cognitive predictors of treatment outcome in cannabis dependence.

BACKGROUND: Drug-related outcomes expectancies and refusal self-efficacy are core components of Social Cognitive Theory. Both predict treatment outcome in alcohol use disorders. Few studies have reported expectancies and refusal self-efficacy in cannabis dependence. None have examined both, although both constructs are key targets in Cognitive-Behavioural Therapy (CBT). This study tests the predictive role of expectancies and refusal self-efficacy in treatment outcome for cannabis dependence. DESIGN: Outpatients completed a comprehensive assessment when commencing cannabis treatment and predictors of treatment outcome were tested. SETTING: A university hospital alcohol and drug outpatient clinic. PARTICIPANTS: 221 cannabis-dependent patients participated in a 6-week CBT program where the goal was abstinence. MEASUREMENTS: Cannabis Expectancy Questionnaire and Cannabis Refusal Self-Efficacy Questionnaire, cannabis dependence severity [Severity of Dependence Scale], psychological distress [General Health Questionnaire] at baseline; the timeline follow-back procedure at baseline and each session. FINDINGS: Patients reporting lower confidence in their ability to resist cannabis during high negative affect (emotional relief refusal self-efficacy) had a lower likelihood of abstinence (p=0.004), more days of use (p<0.001), and larger amount used (p<0.001). Negative cannabis expectancies predicted greater likelihood of abstinence (p=0.024). Higher positive expectancies were associated with lower emotional relief self-efficacy, mediating its association with outcome (p<0.001). CONCLUSIONS: Emotional relief refusal self-efficacy and negative expectancies are predictive of better treatment outcomes for cannabis dependence. Positive expectancies may indirectly predict poorer outcome because of a negative association with self-efficacy, but this conclusion remains tentative as directionality could not be established.

Treatment seeking in cannabis dependence: The role of social cognition.

BACKGROUND AND AIMS: Relatively few cannabis dependent individuals seek treatment and little is known about the determinants of treatment seeking. Social Cognitive Theory (SCT) provides a useful framework for examining human behaviour and motivation which may be helpful in explaining treatment seeking. This study examined the differences in cannabis outcome expectancies and cannabis refusal self-efficacy between treatment seekers and non-treatment seekers with cannabis dependence. DESIGN: Non-treatment seekers were referred to an illicit drug diversion program. Treatment seekers commenced an outpatient cannabis treatment program and completed a comprehensive assessment that included measures of cannabis outcome expectancies and refusal self-efficacy. SETTING: A public hospital alcohol and drug outpatient clinic. PARTICIPANTS: 269 non-treatment seekers and 195 individuals commencing cannabis dependence treatment. MEASUREMENTS: The Cannabis Expectancy Questionnaire (CEQ), Cannabis Refusal Self-Efficacy Questionnaire (CRSEQ), Severity of Dependence Scale - Cannabis (SDS-C), General Health Questionnaire (GHQ-28) and Readiness to Change Questionnaire (RTC) were completed. FINDINGS: Treatment seekers had significantly higher levels of negative cannabis outcome expectancies and significantly lower levels of emotional relief refusal self-efficacy (belief in ability to resist using cannabis when experiencing negative affect) (ps<0.001). Treatment seekers had significantly higher levels of psychological distress and self-perceived cannabis dependence compared to non-treatment seekers (ps<0.001). CONCLUSIONS: High negative cannabis outcome expectancies and low emotional relief refusal self-efficacy may play a key role in motivation to seek treatment.

Craving Mediates Stress in Predicting Lapse During Alcohol Dependence Treatment.

BACKGROUND: Stress, craving, and depressed mood have all been implicated in alcohol use treatment lapses. Few studies have examined all 3 factors. Progress has been limited because of difficulties with craving assessment. The Alcohol Craving Experience Questionnaire (ACE) is a new measure of alcohol craving. It is both psychometrically sound and conceptually rigorous. This prospective study examines a stress-treatment response model that incorporates mediation by craving and moderation by depressed mood and pharmacotherapy. METHODS: Five hundred and thirty-nine consecutively treated alcohol-dependent patients voluntarily participated in an abstinence-based 12-week cognitive-behavioral therapy (CBT) program at a hospital alcohol and drug outpatient clinic. Measures of stress, craving, depressed mood, and alcohol dependence severity were administered prior to treatment. Treatment lapse and treatment dropout were assessed over the 12-week program duration. RESULTS: Patients reporting greater stress experienced stronger and more frequent cravings. Stronger alcohol craving predicted lapse, after controlling for dependence severity, stress, depression, and pharmacotherapy. Alcohol craving mediated stress to predict lapse. Depressed mood and anticraving medication were not significant moderators. CONCLUSIONS: Among treatment seeking, alcohol-dependent patients, craving mediated the relationship between stress and lapse. The effect was not moderated by depressed mood or anticraving medication.

Alexithymia in relation to alcohol expectancies in alcohol-dependent outpatients.

Alexithymia and alcohol outcome expectancies were investigated in 355 alcohol-dependent treatment seekers. Patients with alexithymia gave stronger self-report ratings of expectancies of affective change related to beliefs that alcohol leads to negative mood states and assertion, that alcohol enhances social skills, compared to those without alexithymia. The findings suggest that alcohol-dependent outpatients with alexithymia may drink to experience intensified negative emotions and improved social functioning.

A longitudinal mediational study on the stability of alexithymia among alcohol-dependent outpatients in cognitive-behavioral therapy.

Alexithymia is characterized by difficulty identifying feelings, difficulty describing feelings, and an externally oriented thinking style. Alexithymia has been described as a trait-like risk factor for the development of alcohol use disorders. Few studies have investigated the absolute (whether mean scores change over time) and relative (extent to which relative differences among individuals remain the same over time) stability of alexithymia among men and women with alcohol dependence, or have considered potential underlying mechanisms. Social learning processes contribute to and maintain alcohol problems. The reinforcement of alcohol expectancies is one plausible mechanism that links the difficulties in emotional processing associated with alexithymia and alcohol use. The present study investigated the stability of alexithymia as well as alcohol expectancy as a mediator of alexithymia. Three hundred fifty-five alcohol-dependent patients were enrolled in a cognitive behavioral treatment program. Ninety-two alcohol-dependent patients completed assessments at baseline and at 3-month follow-up. Results indicated that total Toronto Alexithymia Scale (TAS-20; Bagby, Parker, & Taylor, 1994) mean score, difficulty identifying feelings, and difficulty describing feelings decreased significantly over time with a larger decrease in alexithymia mean scores for females. Externally oriented thinking mean scores did not change. The TAS-20 and its subfactors demonstrated significant correlations, from baseline to follow-up, which were stronger for males than for females. Regression analyses showed that the total TAS-20 mean scores, difficulty identifying feelings, and difficulty describing feelings were partially mediated through assertion alcohol expectancies. In conclusion, this suggests that alexithymia has relative stability and is a trait-like factor among alcohol-dependent treatment seekers.