Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention

A Machine Learning Approach

Bibliographic Data

ID21646679
AuthorsSiyuan Huang (0000-0001-6048-8763, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina (MUSC), Charleston, South Carolina, USA), Amy E Wahlquist (0000-0002-6134-0573, East Tennessee State University), Amy Wahlquist (East Tennessee State University), Jennifer Dahne (0000-0001-7297-9420, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina (MUSC), Charleston, South Carolina, USA, corresponding author)
Year2024
Volume59
Issue11
Pages1620-1628
Publication date2024-09-18
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueSubstance Use & Misuse (JOURNAL)
Journal identifiersISSN: 1082-6084 • E-ISSN: 1532-2491
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10826084.2024.2369155
PMID38898605
OpenAlexW4399868323
LanguageEN
References cited63

Background: Depression is prevalent among individuals who smoke cigarettes and increases risk for relapse. A previous clinical trial suggests that Goal2Quit, a behavioral activation-based smoking cessation mobile app, effectively increases smoking abstinence and reduces depressive symptoms. Objective: Secondary analyses were conducted on these trial data to identify predictors of success in depression-specific digitalized cessation interventions. Methods: Adult who smoked cigarettes (age = 38.4 ± 10.3, 53% women) were randomized to either use Goal2Quit for 12 weeks (N = 103), paired with a 2-week sample of nicotine replacement therapy (patch and lozenge) or to a Treatment-As-Usual (TAU) control (N = 47). The least absolute shrinkage and selection operator was utilized to identify a subset of baseline variables predicting either smoking or depression intervention outcomes. The retained predictors were then fitted via linear regression models to determine relations to each intervention outcome. Results: Relative to TAU, only individuals who spent significant time using Goal2Quit (56 ± 46 min) were more likely to reduce cigarette use by at least 50% after 12 weeks, whereas those who spent minimal time using Goal2Quit (10 ± 2 min) did not exhibit significant changes. An interaction between educational attainment and treatment group revealed that, as compared to TAU, only app users with an educational degree beyond high school exhibited significant reductions in depression. Conclusions: The findings highlight the importance of tailoring depression-specific digital cessation interventions to individuals’ unique engagement needs and educational level. This study provides a potential methodological template for future research aimed at personalizing technology-based treatments for cigarette users with depressive symptoms

Abstinence · Behavioral Activation · Cognition · Physical therapy · Psychiatry · Psychological intervention · Randomized controlled trial · Smoking cessation · Behavioral Health and Interventions · Clinical Psychology · Digital Mental Health Interventions · Medicine · Smoking Behavior and Cessation · Internal Medicine

Tools

Open DOIOpen Access