Feasibility of a primary care patient decision aid for smoking cessation with information about e-cigarettes

Context: Patients who smoke are increasingly using e-cigarettes for smoking cessation. Decision aids can promote the delivery of evidence-based smoking cessation treatment in primary care settings; however, more information is needed on the impact of smoking cessation decision aids which include information on switching completely from cigarettes to e-cigarettes.

Objective: To assess the feasibility and acceptability of a smoking cessation decision aid that includes e-cigarette information.

Study Design: Pre-post.

Setting: Primary care.

Population Studied: Adult patients who are current cigarette smokers (N=120).

Intervention: In Phase I (N=60), all patients were shown a decision aid with information about FDA-approved cessation methods (i.e., nicotine replacement therapy, prescription medications) prior to their clinic visit. In Phase II (N=60), current smokers who were e-cigarette users and those with no intention of quitting cigarettes received decision aids with additional information on switching to e-cigarettes.

Outcome Measures: Quit attempts and abstinence, confidence and readiness to quit, confidence and readiness to switch to e-cigarettes, and patient satisfaction.

Results: Patients reported higher confidence and readiness to quit after viewing the decision aid and consulting with their physician across both phases (p<.05). Patients reported the decision aid helped prepare them to make a decision about quitting smoking and expressed satisfaction with the decision aid and physician consultation. There was no impact of including e-cigarette information in the decision aids on any measured outcomes.

Conclusions: Smoking cessation decision aids are acceptable to patients and may promote behavior change. Further research is needed to identify strategies to promote shared decision-making regarding e-cigarettes. Future studies should explore the impact of providing patients e-cigarette information using larger sample sizes and rigorous designs.