Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: Substance use is known to be episodic, dynamic, complex, and highly influenced by the environment, therefore a situational and momentary focus to alcohol craving research is appropriate. Current advances in mobile and wearable technology provide novel opportunities for craving research. However, the lack of consensus within craving theory impedes the identification and prioritization of parameters to be monitored. The aim of this study is to critically review current craving models in order to determine viable theoretical frameworks of alcohol craving and its essential parameters. Methods: Eighteen models of craving were reviewed by applying a literature search with a five-step strategy that accounted for the momentary nature of craving and included a snowballing search and a key term extraction algorithm. Based on this review, multiple decision criteria were defined upon which to evaluate the models. Results: Six models for alcohol craving were supported by sufficient empirical research to be eligible. The inferences drawn on these six models resulted in three decision criteria: the model should (1) incorporate negative affect as a predictor of relapse; (2) explain that dependent drinkers have a higher attentional bias towards alcohol cues than nondependent drinkers; (3) incorporate increased risk of relapse with heightened stress levels. Conclusions: The affective processing model of negative reinforcement, the cognitive processing model, the incentive sensitization theory of addiction and the theory of neural opponent motivation are classified as viable theoretical frameworks, resulting in negative affect and stress as relevant parameters to include in real-time craving monitoring research.

In the past decades, monitoring programs have been developed for healthcare professionals with substance use disorders. We aimed to explore estimates of abstinence and work retention rates after participation in such monitoring programs. A literature search was performed using PubMed, Embase, PsycINFO, and CINAHL. Twenty-nine observational studies reporting on success rates (abstinence and work retention) of monitoring for healthcare professionals with a substance use disorder were included in the meta-analysis. Quality-effects models calculated pooled success rates and corresponding 95%-Confidence Intervals (CI), with subgroup analyses on monitoring elements and patient characteristics. Pooled success rates were 72% for abstinence (95%-CI = 63–80%) and 77% for work retention (95%-CI = 61–90%). Heterogeneity across studies was partly explained by the starting moment of monitoring, showing higher abstinence rates for studies that started monitoring after treatment completion (79%; 95%-CI = 72–85%) compared to studies that started monitoring with treatment initiation (61%; 95%-CI = 50–72%). About three-quarters of healthcare professionals with substance use disorders participating in monitoring programs are abstinent during follow-up and working at the end of the follow-up period. Due to selection and publication bias, no firm conclusions can be drawn about the effectiveness of monitoring for healthcare professionals with SUD.

Background: The inability of individuals with Alcohol Use Disorders (AUD) to recognize and describe their feelings and cravings may be due to alexithymia. Previous researches have shown evidence for a negative influence of alexithymia on treatment outcomes in patients with AUD. Therefore, it was hypothesized that high alexithymic patients with AUD would benefit less from cognitive behavioral therapy (CBT) compared with low alexithymic patients. Methods: One hundred alcohol-dependent inpatients (DSM IV) were assessed with the Mini International Neuropsychiatric Interview for psychiatric disorders, the Toronto Alexithymia Scale (TAS-20), and the European Addiction Severity Index (EuropASI). Baseline alexithymia, as a categorical and continuous variable, was used to compare or relate baseline demographic and addiction characteristics, time in treatment, abstinence, and differences in addiction severity at 1-year follow-up. Analyses were performed using χ² test, analysis of variance or Kruskal–Wallis, paired t-tests or Wilcoxon’s signed rank tests, multivariate logistic, and linear regression models, as appropriate. Results: The prevalence of high alexithymia (TAS-20 > 61) was 45%. The total TAS-20 score correlated negatively with years of education (r = −.21; p = .04) and positively with the psychiatry domain of the EuropASI (r = .23; p = .04). Alexithymia showed no relation to abstinence, time in treatment, or change in severity of alcohol-related problems on the EuropASI. Conclusion: High alexithymic patients with AUD do benefit equally from inpatient CBT-like treatment as low alexithymic patients with AUD. Scientific significance: Multimethod alexithymia assessments with an observer scale have been advised to judge the relationship with resulting outcome in CBT.