Automatic Attitudes and Health Information Avoidance

Objective: Early detection of disease is often crucially important for positive health outcomes, yet people sometimes decline opportunities for early detection (e. g., opting not to screen). Although some health-information avoidance reflects a deliberative decision, we propose that information avoidance can also reflect an automatic, nondeliberative reaction. In the present research, we investigated whether people's automatic attitude toward learning health information predicted their avoidance of risk feedback. Method: In 3 studies, we gave adults the opportunity to learn their risk for a fictitious disease (Study 1), melanoma skin cancer (Study 2), or heart disease (Study 3), and examined whether they opted to learn their risk. The primary predictors were participants' attitudes about learning health information measured using a traditional (controlled) self-report instrument and using speeded (automatic) self-report measure. In addition, we prompted participants in Study 3 to contemplate their motives for seeking or avoiding information prior to making their decision. Results: Across the 3 studies, self-reported (controlled) and implicitly measured (automatic) attitudes about learning health information independently predicted avoidance of the risk feedback, suggesting that automatic attitudes explain unique variance in the decision to avoid health information. In Study 3, prompting participants to contemplate their reasons for seeking versus avoiding health information reduced information avoidance. Surprisingly, it did so by inducing reliance on automatic, rather than controlled, attitudes. Conclusion: The data suggests that automatic processes play an important role in predicting health information avoidance and suggest that interventionists aiming to increase information seeking might fruitfully target automatic processes.