H. Jonathon Rendina, PhD, MPH
2018 Emerging Leader Award
H. Jonathon Rendina, PhD, MPH, received the 2018 Psychology and AIDS Distinguished Leader Award as a Emerging Leader for "outstanding dedication to innovative, cutting-edge research, exceptional teaching, and contributions to HIV/AIDS prevention science."
Rendina is an Assistant Professor in the Department of Psychology, Hunter College, City University of New York (CUNY). He is also the Director of Quantitative Methods and a Faculty Investigator at the Center for HIV Educational Studies & Training, Hunter College, CUNY. Rendina’s research is broadly focused on the impact of social stress on health, and I am currently pursuing research looking at how HIV-related stressors influences the mental, behavioral, and physical health of HIV-positive gay and bisexual men. This work is informed by the minority stress model and seeks to integrate HIV-related and sexual minority stressors into a unified model of health for this population. Much of his research uses online and mobile technologies, particularly intensive longitudinal designs such as ecological momentary assessment (EMA), and his long-term goal is to develop and test mobile health (i.e., mHealth) interventions aimed at reducing the impact of social stress on health. I am also interested in the role of emotions as mediators and moderators of the stress-health association, with a particular emphasis on how emotional processing and its interaction with cognitive processing might help to explain why and for whom there is a he is also actively involved in several others lines of research, including: (1) developing event-level models of sexual decision making that integrate both cognitive and affective processes; (2) predictors, consequences, and patterns of substance use and abuse; and (3) HIV prevention, particularly pre-exposure prophylaxis (PrEP), and modeling trends over time in PrEP acceptability, uptake and suspension. He maintains a particularly heavy emphasis on research methods and statistics, and regularly utilize methods such as multilevel modeling (MLM), factor analysis, structural equation modeling (SEM), psychometric analysis and latent class analysis (LCA).