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Past research has demonstrated that ultraviolet (UV) photos - which reveal skin damage as dark patches - can increase preventive behaviors. Emerging camera technology facilitates personalized UV photos for interventions, yet little is known about how personalized photos compare to other visuals and what cognitive or affective mechanism explains their persuasive impact. To engage this research line, the current study compared the impact of personalized UV (PUV), stock UV (SUV), and non-UV (NUV) photos and, to advance theorizing on fear appeals, explored underlying affective mechanisms including physiological fear. A sample of 169 undergraduate students participated in a 3 (Visual conditions: PUV, SUV, NUV) × 2 (Efficacy conditions: No efficacy and Efficacy) between-participants message experiment on a computer equipped with iMotions 6.4 that tracked real-time physiological responses (facial expression and skin conductance). Results demonstrated that PUV skin damage photos produced significantly greater self-reported fear and positive valence (detected by facial expression analysis) than NUV and SUV visuals. Mediation analysis demonstrated that fear had a significant indirect effect on the relationship between exposure to PUV skin damage visuals and behavior expectations.

In this article, we present strategies for collecting and coding a large longitudinal communication data set collected across multiple sites, consisting of more than 2000 hours of digital audio recordings from approximately 300 families. We describe our methods within the context of implementing a large-scale study of communication during cancer home hospice nurse visits, but this procedure could be adapted to communication data sets across a wide variety of settings. This research is the first study designed to capture home hospice nurse-caregiver communication, a highly understudied location and type of communication event. We present a detailed example protocol encompassing data collection in the home environment, large-scale, multisite secure data management, the development of theoretically-based communication coding, and strategies for preventing coder drift and ensuring reliability of analyses. Although each of these challenges has the potential to undermine the utility of the data, reliability between coders is often the only issue consistently reported and addressed in the literature. Overall, our approach demonstrates rigor and provides a "how-to" example for managing large, digitally recorded data sets from collection through analysis. These strategies can inform other large-scale health communication research.