A Tailoring Algorithm to Optimize Behavior Change

Effective computerized tailoring can enhance the impact of health interventions. Long-term success rates can be improved with prospective tailoring that builds on evidence-based research. A new algorithm, developed with data from smoking cessation clinical trials and published practice guidelines, is designed to predict the likelihood of abstinence. The algorithm prioritizes the content of a stop-smoking intervention individually for each user and indicates the potential effect of utilizing various stop-smoking medications and stop-smoking approaches. Thus, it has the potential to guide a smoker through the cessation process by dynamically optimizing the likelihood of success. Importantly, the algorithm predicts that even a daily smoker may be able to substantially improve the likelihood of quitting and staying quit both by using stop-smoking techniques and medications and by addressing emotional and cognitive issues that sustain smoking.