A multi-site proof-of-concept investigation of computerized approach-avoidance training in adolescent cannabis users.
rct · Level II
Where this comes from
- Record sourced from PubMed, PMID 29679914.
- Also identified by DOI 10.1016/j.drugalcdep.2018.03.007 and PMC identifier 6053264.
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Abstract
Few effective treatment options exist for cannabis-using youth. This pilot study aimed to test Approach-Avoidance Training to reduce cannabis use with non-treatment-seeking adolescents. Eighty cannabis-using non-treatment-seeking adolescents (average age 19) were recruited from San Diego, California and Charleston, South Carolina, and randomized to complete either six sessions of Cannabis Approach-Avoidance Task Training (CAAT-training) designed to reduce automatic approach biases for cannabis cues or CAAT-sham training. Change in two primary outcome variables was examined: 1) cannabis approach bias and 2) percent cannabis use days over study enrollment. Change in percent alcohol use days over study enrollment was explored as a secondary outcome. A mixed models repeated measures analysis confirmed the group by time interaction effect for approach bias failed to reach statistical significance (p = .06). Significant group by time interaction effects (ps < 0.05) predicted percent days of cannabis and alcohol use over study enrollment. Participants randomized to the avoid cannabis condition (CAAT-training) reported 7% fewer days of cannabis use compared to 0% change for sham; unexpectedly, those in the avoid cannabis condition reported 10% percent more alcohol use days compared to 3% more for sham. Computerized cognitive bias modification paradigms may have utility in reducing adolescent cannabis use. Future work should consider developing a paradigm that addresses both cannabis and alcohol, as well as alternative computerized approaches for coping with addictive behavior in conjunction with bias modification.
Medical subject headings
- Adolescent Behavior
- Avoidance Learning
- Marijuana Abuse
- Proof of Concept Study
- Therapy, Computer-Assisted