Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach

Abstract

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.

Keywords:

• Smoking cessation
• depression
• digital health
• personalized intervention
• machine learning

Acknowledgments

The authors would like to thank MountainPass Technology for their partnership in developing Goal2Quit and study staff members, including Noelle Natale, Johanna Hidalgo and Monika Schindwolf, for assistance in data collection.

Author contributions

Concept and design: Dahne, Huang

Drafting of the manuscript: Huang

Critical revision of the manuscript for important intellectual content: All authors

Statistical analysis: Huang

Obtained funding: Dahne

Statistical support: Wahlquist

Supervision: Dahne

Declaration of interest

Dr. Dahne is a co-owner of Behavioral Activation Tech LLC, a small business that develops digital interventions for behavioral health treatment. Behavioral Activation Tech does not have a financial interest in Goal2Quit. Intellectual property for Goal2Quit is owned by the Medical University of South Carolina.

Data availability statement

The datasets and the proposed method’s R codes generated during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

Funding for this research was provided by the National Institute on Drug Abuse (K23 DA045766). The funding source had no role in study design, data collection, data analysis, data interpretation, in writing this report, or in the decision to submit this article for publication.