Developing Web-based Implicit Cognition Measures and Applying them in E-health Projects (MSW.7353)

Why do people drink too much alcohol, smoke and use other substances, despite knowing that this is bad for them? And how can we change these addictive behaviors? This project combines and integrates two recent developments in applied cognitive science: 1. the assessment of implicit cognition in addictive behaviors; 2. the development of (web-based) e-health interventions to change these addictive behaviors. Until recently, cognitive research on motives for use and expected effects of use relied on explicit questionnaires that ask people to introspect about their reasons for use. More recently, researchers have begun to use implicit measures to assess alcohol-related cognitions. In these measures peoples’ associations and motives are derived on the basis of differences in respondents’ reaction times for classifying relevant stimuli. Research by the applicant and others has demonstrated that these so called implicit cognitions predict behavior (i.e. substance use) above what is predicted with explicit measures. In other words, we are able to measure people’s implicit cognitions that partly steer their behavior, but to which they have little or no introspective access. In the proposed research, this technique is applied in a web-based context (as has already been done successfully in the U.S.), and linked to web-based health interventions (which has not been done before). In current web-based interventions clients get tailored feedback about their health behaviors, and strategies to change these behaviors. Initial results are promising. Here, we add the component of feedback about one’s implicit cognitions to the web-based intervention. We hypothesize this to have two positive effects: first, will make web-based health interventions more attractive to high-risk youth and second, the feedback about implicit associations is expected to help to change addictive behaviors.