Abstract
INTRODUCTION: Theoretically, a cancer diagnosis has the potential to spur health behavior changes in physical activity, diet, substance use, medication adherence, and the like. The Teachable Moment heuristic is a parsimonious, transtheoretical framework for understanding the conditions under which behavior change might occur, with constructs that include affective, cognitive, and social factors. Application of the Teachable Moment to smoking cessation after cancer diagnosis might aid selection of predictors in observational studies and inform how to optimally design interventions to promote quit attempts and sustain abstinence, as many smoking cessation interventions for cancer survivors do not yield positive outcomes. AIMS AND METHODS: This scoping review of 47 studies that span nearly 20 years of literature examines the measurement of the Teachable Moment constructs and what empirical support they have in explaining cancer survivors' smoking behavior. RESULTS: From this review, it appears the construct of affective response is more widely explored than risk perceptions, social role, and self-concept. Strong, negative affective responses (e.g., anxiety, general distress) may be a powerful contributor to continued smoking after a cancer diagnosis. Risk perceptions may also play a role in smoking behavior, such that never and former smokers espouse stronger perceptions of smoking-related risks than current smokers. Finally, due to a paucity of studies, the role of cancer survivors' self-concept (e.g., identity as a "cancer survivor") and changes in their social role (e.g., employee, athlete) are unclear contributors to their smoking behavior. In summary, the Teachable Moment holds promise in its application to smoking cessation after a cancer diagnosis, though more direct research is needed. CONCLUSIONS: This scoping review of the scientific literature is the first formal test of the extent to which cancer diagnosis has been explored as a "teachable moment" for smoking cessation, with results that provide insight into issues of measurement precision and breadth as well as empirical support of the "teachable moment" heuristic.