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Background: The Internet has increasingly been studied as mode of delivery for interventions targeting problematic alcohol use. Most interventions have been fully automated, but some research suggests that adding counselor guidance may improve alcohol consumption outcomes. Methods: An eight-module Internet-based self-help program based on cognitive behavioral therapy (CBT) was tested among Internet help-seekers. Eighty participants with problematic alcohol use according to the Alcohol Use Disorders Identification Test (AUDIT; scores of ≥ 6 for women and ≥ 8 for men) were recruited online from an open access website and randomized into three different groups. All groups were offered the same self-help program, but participants in two of the three groups received Internet-based counselor guidance in addition to the self-help program. One of the guidance groups was given a choice between guidance via asynchronous text messages or synchronous text-based chat, while the other guidance group received counselor guidance via asynchronous text messages only. Results: In the choice group, 65% (13 of 20 participants) chose guidance via asynchronous text messages. At the 10-week post-treatment follow-up, an intention-to-treat (ITT) analysis showed that participants in the two guidance groups (choice and messages) reported significantly lower past week alcohol consumption compared to the group without guidance; 10.8 (SD = 12.1) versus 22.6 (SD = 18.4); p = 0.001; Cohen’s d = 0.77. Participants in both guidance groups reported significantly lower scores on the AUDIT at follow-up compared to the group without guidance, with a mean score of 14.4 (SD = 5.2) versus 18.2 (SD = 5.9); p = 0.003; Cohen’s d = 0.68. A higher proportion of participants in the guidance groups said that they would recommend the program compared to the group without guidance (81% for choice; 93% for messages versus 47% for self-help). Conclusion: Self-help programs for problematic alcohol use can be more effective in reducing alcohol consumption over a 10-week period when counselor guidance is added. Trial Registration: Clinicaltrials.gov [ABSTRACT FROM AUTHOR]

Introduction: Cannabis is Europe's most commonly used illicit drug. Some users do not develop dependence or other problems, whereas others do. Many factors are associated with the occurrence of cannabis-related disorders. This makes it difficult to identify key risk factors and markers to profile at-risk cannabis users using traditional hypothesis-driven approaches. Therefore, the use of a data-mining technique called binary recursive partitioning is demonstrated in this study by creating a classification tree to profile at-risk users. Methods: 59 variables on cannabis use and drug market experiences were extracted from an internet-based survey dataset collected in four European countries (Czech Republic, Italy, Netherlands and Sweden), n = 2617. These 59 potential predictors of problematic cannabis use were used to partition individual respondents into subgroups with low and high risk of having a cannabis use disorder, based on their responses on the Cannabis Abuse Screening Test. Both a generic model for the four countries combined and four country-specific models were constructed. Results: Of the 59 variables included in the first analysis step, only three variables were required to construct a generic partitioning model to classify high risk cannabis users with 65–73% accuracy. Based on the generic model for the four countries combined, the highest risk for cannabis use disorder is seen in participants reporting a cannabis use on more than 200 days in the last 12 months. In comparison to the generic model, the country-specific models led to modest, non-significant improvements in classification accuracy, with an exception for Italy (p = 0.01). Conclusion: Using recursive partitioning, it is feasible to construct classification trees based on only a few variables with acceptable performance to classify cannabis users into groups with low or high risk of meeting criteria for cannabis use disorder. The number of cannabis use days in the last 12 months is the most relevant variable. The identified variables may be considered for use in future screeners for cannabis use disorders. [ABSTRACT FROM AUTHOR]