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Objective. To examine whether the addition of acamprosate to Cognitive Behavioural Therapy (CBT) outpatient alcohol dependence treatment impacted on subjective health status. Method. Among 268 patients consecutively treated for alcohol dependence, 149 chose CBT alone. A matched design was used. From a possible pool of 119 Acamprosate + CBT and 149 CBT-only patients, 86 Acamprosate + CBT subjects were individually matched with 86 CBT-only patients on parameters of gender, age, prior detoxification and alcohol dependence severity. Health Status (SF-36) and Psychological Well-Being (GHQ-28) was assessed pre- and post-treatment. Results. Pre-treatment, both self-reported health status and psychological well-being was markedly below normative (community) ranges. Program completers significantly improved across both measures over 12 weeks of treatment and some health domains approximated community levels. No treatment group differences were observed. Conclusions. Participants who completed the CBT-based treatment showed significant improvement in self-reported health status. The use of acamprosate did not register additional improvement on either SF-36 or GHQ-28, beyond CBT alone. doi:10.1300/J069v25n04_05. [ABSTRACT FROM AUTHOR]

BACKGROUND: The anti-craving drug, naltrexone, is used as a pharmacotherapeutic adjunct in the treatment of alcohol dependence. In addictive disorders, compliance issues remain central. There are limited data on compliance with naltrexone treatment regimens within formalized rehabilitation programs and even less data that identifies factors that have an impact on this. OBJECTIVE: To study patient adherence to naltrexone medication regimens and examine whether patients' reported pre-treatment alcohol use, dependence severity and measures of psychological health are predictive of medication compliance. METHOD: Fifty outpatients meeting DSM IV criteria for alcohol dependence enrolled in a 12-week rehabilitation programme. This included cognitive behavioural therapy (CBT) and naltrexone, 50 mg orally daily. Measures included: pharmacy prescription pick-up including number of tablets dispensed, programme attendance and patient pre-treatment alcohol use variables. Measures of psychological health included somatic symptoms, anxiety, social dysfunction and depression as measured by the General Health Questionnaire (GHQ-28). RESULTS: Classifying the sample into compliant (> or = 90% medication pick-up) and less compliant groups, 66% of subjects were naltrexone-compliant. Pre-treatment alcohol use variables were not predictive of compliance. Although social dysfunction and depression tended towards poorer prescription filling, measures of psychological distress (GHQ-28) did not identify factors predictive of medication non-compliance. One patient withdrew from treatment because of naltrexone-induced dysphoria. CONCLUSION: Patients with alcohol dependence demonstrated high levels of anti-craving medication compliance, good rehabilitation programme participation and favourable outcomes. Naltrexone was well tolerated. Medication compliance in this study group compared well with those of other hospital populations with chronic disorders. Factors predictive of anti-craving medication compliance in alcohol dependence require further study. [ABSTRACT FROM AUTHOR]

Are acamprosate and naltrexone safe and effective treatments for adults with alcohol dependence?METHODSDesign: Systematic review with meta-analysis.Data sources: CINAHL, EMBASE, MEDLINE, and the Cochrane Controlled Trials Register (January 1990 to September 2002) plus hand searches of reference lists.Study selection and analysis: Eligible studies were randomised controlled trials comparing naltrexone or acamprosate with placebo or a control group not taking any medication. Exclusions: <10 people in the study; less than 2 weeks' follow up; lack of relevant primary clinical data; or no quantitative results. Data were extracted on study scope and design, randomisation process, sample size, intervention, exclusion and inclusion criteria, baseline characteristics, length of treatment and follow up, outcome, and treatment compliance. A random effects model was used to carry out meta-analysis. Peto's odds ratios were used to measure dichotomous outcomes and weighted mean differences were used to measure continuous outcomes.Outcomes: Relapse and abstinence rates; treatment compliance and duration of abstinence.MAIN RESULTSThirty three studies met inclusion criteria. Thirteen studies compared acamprosate with placebo, 19 studies compared naltrexone with a control group (placebo or reference group), and one study compared naltrexone with acamprosate. Acamprosate significantly improved abstinence rate and treatment compliance compared with placebo (see http://www.ebmentalhealth.com/supplemental for table). Acamprosate significantly increased the duration of abstinence compared with placebo (weighted mean difference: 27 days; SD 18-36.5 days). Acamprosatc produced few side effects. The most common adverse events were gastrointestinal problems. In the short term, naltrexone significantly reduced relapse rate but not abstinence rate compared with controls (see table). Few studies reported the effects of long term naltrexone use. Naltrexone caused frequent side effects, however these did not significantly lower adherence to treatment.CONCLUSIONSNaltrexonc and acamprosate are both safe and effective treatments for alcohol dependence in adults. Acamprosate improves adherence and abstinence rates, whereas naltrexone significantly reduces relapse rates, time to relapse, and frequency of drinking. However, the high level of non-compliance for both treatments may limit their usefulness.NOTESAll studies involved some form of psychosocial intervention in combination with naltrexone or acamprosate that varied widely between studies. [ABSTRACT FROM AUTHOR]

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., (© 2022 Research Society on Alcoholism.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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 (β = -0.27, p = .008), Opportunistic (β = -0.24, p = .013) and Social Facilitation (β = -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., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Gullo, Papinczak, Feeney, Young and Connor.)

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 (M age  = 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., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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., (© 2020 Society for the Study of Addiction.)

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.

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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.

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Despite considerable research activity and application in treatment, the construct of craving remains poorly understood. We propose that cravings and urges are cognitive-emotional events in time, characterised by frequency, duration, intensity and salience. Commonly used measures of alcohol craving are reviewed, and their strengths and weaknesses identified. Most measures confound craving with behaviours, or with separable cognitive phenomena such as expectancies, intentions, or perceived behavioural control. These confounds have limited our advances in understanding the determinants and consequences of craving. Based on the criteria applied in this review, among the better performing multi-item measures are the Penn Alcohol Craving Scale and Obsessive subscale of the Obsessive-Compulsive Drinking Scale. Optimal assessment strategies are likely to involve daily assessments of peak intensity of cravings, desires or urges and of the frequency and duration of craving episodes. Of particular interest are measures of intensity at times when individuals are at risk of drinking or of other functional impacts from craving., (Copyright © 2012 Elsevier Ltd. All rights reserved.)