Translation of associative learning models into extinction reminders delivered via mobile phones during cue exposure interventions for substance use.

Published

Journal Article (Review)

Despite experimental findings and some treatment research supporting the use of cues as a means to induce and extinguish cravings, interventions using cue exposure have not been well integrated into contemporary substance abuse treatments. A primary problem with exposure-based interventions for addiction is that after learning not to use substances in the presence of addiction cues inside the clinic (i.e., extinction), stimuli in the naturalistic setting outside the clinic may continue to elicit craving, drug use, or other maladaptive conditioned responses. For exposure-based substance use interventions to be efficacious, new approaches are needed that can prevent relapse by directly generalizing learning from the therapeutic setting into naturalistic settings associated with a high risk for relapse. Basic research suggests that extinction reminders (ERs) can be paired with the context of learning new and more adaptive conditioned responses to substance abuse cues in exposure therapies for addiction. Using mobile phones and automated dialing and data collection software, ERs can be delivered in everyday high-risk settings to inhibit conditioned responses to substance-use-related stimuli. In this review, we describe how associative learning mechanisms (e.g., conditioned inhibition) can inform how ERs are conceptualized, learned, and implemented to prevent substance use when delivered via mobile phones. This approach, exposure with portable reminders of extinction, is introduced as an adjunctive intervention that uses brief automated ERs between clinic visits when individuals are in high-risk settings for drug use.

Full Text

Duke Authors

Cited Authors

  • Rosenthal, MZ; Kutlu, MG

Published Date

  • September 2014

Published In

Volume / Issue

  • 28 / 3

Start / End Page

  • 863 - 871

PubMed ID

  • 25134055

Pubmed Central ID

  • 25134055

Electronic International Standard Serial Number (EISSN)

  • 1939-1501

Digital Object Identifier (DOI)

  • 10.1037/a0037082

Language

  • eng

Conference Location

  • United States