Nicotine Use Disorder, Nicotine Vaping
Conditions
Keywords
vaping, mobile app intervention, youth, young adults, chatbot, nicotine, social media
Brief summary
Use of vaping products (e.g., electronic nicotine delivery systems, e-cigarettes) has been increasing rapidly, particularly among teens and young adults. With limited information on the long-term effects of vaping products, health information about vaping has been somewhat unclear in regards to associated health risks. Teens and young adults may be reluctant to disclose their use of vaping products to parents or health providers and instead turn to social media to share and seek out information regarding vaping risks and cessation supports. Thus, our current proposal outlines the use of social media to identify teens and young adults socially networking about vaping, the use of an online chatbot screen to evaluate individual cessation support needs, and the use of a digital intervention system to support vaping cessation. The mobile intervention used in this study is based on a widely-used evidence-based mobile intervention for combustible smoking (i.e., quitSTART) and has been adapted for vaping and young adults to include an in-app chatbot to guide users to tailored content and to motivate and encourage their cessation efforts. We aim to integrate our social media recruitment and online screening approach to connect individuals with this mobile app intervention, and will conduct a randomized controlled trial to evaluate user engagement with and preliminary efficacy of the digital intervention on reducing vaping behaviors among teens and young adults.
Interventions
DEVICEquitSTART Mobile Application
Mobile application with educational content, motivational messages, self-monitoring, and tips for managing cravings to support vaping cessation among young adults.
BEHAVIORALChatbot Feature in quitSTART Mobile Application
In-App chatbot feature emedded into quitSTART mobile application to motivate vaping cessation and support app navigation.
Sponsors
Washington University School of Medicine
The University of Texas Health Science Center, Houston
University of South Carolina
National Institute on Drug Abuse (NIDA)
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
15 Years to 25 Years
Healthy volunteers
No
Inclusion criteria
* US Resident * English Speaking * Have daily access to a smartphone * Current vaping product user (P30D) * Identified as socially networking about a desire to quit vaping and/or experiencing negative health outcomes due to vaping * Vaping product user only (i.e., not using vaping as a means to support combustible smoking cessation)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Vaping cessation | Baseline, 6 weeks, 3 months | Vaping cessation as assessed by 7 day self-reported vaping abstinence at follow-up. Timeline follow back procedures will be used to establish abstinence or if participants lapsed (their first instance of vaping post quit) or relapsed (vaped on 7 consecutive days) at each follow up time point. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Nicotine dependence | Baseline, 6 weeks, 3 months | Nicotine dependence will be assessed using the 4-item Patient-Reported Outcomes Measurement Information System (PROMIS). |
| Risk perception related to vaping | Baseline, 6 weeks, 3 months | Perceived risks of vaping, both general and conditional, will be assessed with two items adapted from Wackowski et al., 2020. "How harmful, if at all, do you think vaping/using an e-cigarette is to a user's health?" (general) and "Imagine you vaped/used e-cigarettes daily for the next 10 years and used no other tobacco product. How harmful do you think this vaping would be to your health?" (conditional), both using a five-point response scale (not at all-extremely harmful). |
| Intervention engagement | Baseline, 6 weeks, 3 months | Data automatically captured within the chatbot, and app platform will be used to calculate a composite measure of engagement for those randomized to each of these intervention components and will be examined as a predictor of outcomes. We will use a modified version of an app Engagement Index (EI). |
Countries
United States
Contacts
CONTACTPatricia Cavazos-Rehg, PhD
CONTACTErin Kasson, MSW
Outcome results
None listed