Lynn Miller (chair)
Michael Cody, Stephen Read
Using Virtual Environments to Unobtrusively Measure Risk-Taking: Findings and Implications for Health Communication Interventions
Would virtual behaviors be predictive of past and future behaviors? Does the relationship between an individual’s virtual and subsequent real life behavior depend on active interactive decision-making or is mere passive observation of another’s choices enough to predict subsequent real-life behavior? Would virtual decisions (and interventions tied to them) predict behavior 3 months subsequently above and beyond traditional cognitive variables (e.g., intent, self-efficacy)? These questions can be addressed by taking a SOLVE (Socially Optimized Learning in Virtual Environments) approach. To address hypotheses related to these questions, 151 men who have sex with men (MSM) filled out initial baseline measures of their past risk-taking behavior (e.g., use of alcohol, drugs, sexual history) and other measures (e.g., demographics, traditional self-report predictors) and were then randomly assigned to an interactive video (IAV-SOLVE) condition or a non-interactive yoked control condition (choices of another MSM). MSM in the Interactive HIV Prevention Video condition made a range of behavioral choices (e.g., drink alcohol, take drugs, or take sexual risks) on a virtual date. The choices were electronically recorded. MSM assigned to the yoked condition passively observed the choices that had been made by another MSM. Participants in both conditions then answered immediate post-measures (e.g., traditional psychosocial health risk predictor variables). After three months, participants filled out a follow-up survey where risk-taking behavior was again re-assessed.
The first set of analyses (Study Set 1) involved only those MSM in the IAV condition and addressed two hypotheses. Consistent with hypothesis 1, a series of Chi square analyses showed that virtual risk-taking was significantly related to past behavior, and consistent with hypothesis 2, it was predictive of future risk-taking. Study Set 2 examined the extent to which virtual decisions accounted for more variance in past or future risk-behavior. To assess the role of interactivity (actually making choices), these hypotheses involved examining these links for participants in both the IAV (where virtual decisions were expected to predict behavior) and Yoked conditions (where simply watching another’s responses were not expected to predict behavior). Simultaneous and hierarchical multiple regression analyses revealed that the TPB significantly accounted for past risk (consistent with H3) and significantly predicted future risk (consistent with H4) in the IAV and Yoked conditions. The predictive role of past behavior in predicting to future behavior (H5) was also confirmed. However, virtual risk-taking also accounted for more unique variance in predicting future risk-taking behavior than the TPB did in the IAV but not in the Yoked condition (consistent with H6 & H7). In addition, (consistent with H8) the two sets of constructs combined (virtual risk and the TPB) fully mediated the relationship between past and future risk-taking behavior in the IAV but not in the yoked condition. These findings suggest that virtual environments using a SOLVE approach may allow us to unobtrusively identify those most at risk in order to tailor more effective interactive interventions to reduce future risk-taking.