Approach Bias Training for Nicotine Addiction

Palo Alto (17 mi)

Overseen byLorra Garey, Ph.D

Age: 18+

Sex: Any

Travel: May be covered

Time Reimbursement: Varies

Trial Phase: N/A

Waitlist Available

Sponsor: University of Houston

No Placebo Group

Trial Summary

What is the purpose of this trial?The present project will evaluate the initial efficacy of approach bias retraining among dual combustible cigarette (CC) and electronic cigarette (ECIG) users. The study employs a randomized controlled design to follow 90 experienced dual CC/ECIG users motivated to quit nicotine as they engage in a self-guided quit attempt following approach bias retraining.

Eligibility Criteria

This trial is for adults who've used both regular cigarettes and e-cigarettes with nicotine almost every day over the last month, can read and speak English fluently, want to quit nicotine (scoring at least 5 out of 10 in motivation), and own an Android smartphone. It's not for those currently getting treatment for mental illness or addiction, using other quitting aids like nicotine patches or certain medications, or unable to consent.

Treatment Details

The study tests a 'bias training' method to help people quit smoking by comparing three groups: one uses bias training for both cigarettes and e-cigs (CC+ECIG Condition), another only for cigarettes (CC Condition), while the third group gets a fake intervention (Sham Condition). Participants are randomly assigned to these groups during their self-guided attempt to stop using nicotine.

3Treatment groups

Experimental Treatment

Active Control

Placebo Group

Group I: CC+ECIG ConditionExperimental Treatment1 Intervention

Combustible Cigarette (CC) and Electronic Cigarette (ECIG) users will be trained to avoid CC and ECIG-related images and approach positive images.

Group II: CC Only ConditionActive Control1 Intervention

Participants in this arm will be trained to avoid only combustible cigarette images, not electronic cigarettes images.

Group III: Sham ConditionPlacebo Group1 Intervention

Participants will pull and push all pictures of CC and ECIG images equally with no preference towards one or the other.