Smoking cessation counseling decision aid with E-cigarette information: A feasibility test

Abstract

Context: E-cigarette use has been increasing rapidly, most prominently among adult smokers. Smokers are more likely to use e-cigarettes to help them quit smoking than FDA-recommended methods (e.g., nicotine replacement therapy or prescription medications). Since at least 70% of smokers visit their physician annually, clinical encounters in primary care present a critical opportunity to provide smokers with evidence-based information about e-cigarettes. Objective: Enhance a previously developed prototype decision aid (DA) for smoking cessation counseling in primary care settings incorporating messages based on the behavior change approach of Motivational Interviewing (MI) and conduct focus groups physicians to further refine and inform the design of the enhanced DA. Study Design: Qualitative study. Setting or Dataset: Health system primary care practices. Population studied: Board-certified primary care physicians, PGY2 or 3 residents, nurse practitioners and physician assistants (N=10). Outcome Measures: Develop an enhanced smoking cessation DA incorporating MI-based content. Results: While most participants had formal training in MI techniques during residency, and sometimes used it for smoking cessation counseling, some still found it challenging to engage smokers in discussions about smoking cessation when they did not want to quit (i.e., precontemplation stage). Physicians acknowledged that time constraints and competing priorities (i.e., patient comorbidities) sometimes limited their ability to use MI techniques. Most physicians were familiar with DAs, although prior experience using them in practice was lacking, and only one participant had used them with patients. Physicians in both focus groups expressed interest in using the DA with patients and commented that it had the potential to improve cessation counseling by spending time building off DA content and getting into the “real work” of smoking cessation and employing MI techniques, rather than focusing on “standard” questions that would have already been answered in the DA. Conclusions: An MI-based