Persuasive Data Videos: Investigating Persuasive Self-Tracking Feedback with Augmented Data Videos - Choe, Eun Kyoung; Sakamoto, Yumiko; Fatmi, Yanis; Lee, Bongshin; Hurter, Christophe; Haghshenas, Ashkan; Irani, Pourang (Proceedings of AMIA 2019, 2019)
Self-tracking feedback with engaging and persuasive visualizations not only helps convey data but can also affect people’s attitudes and behaviors. We investigate persuasive self-tracking feedback by augmenting data videos (DVs)—novel, engaging storytelling media. We introduce a new class of DVs, called Persuasive Data Videos (PDVs), by incorporating four persuasive elements—primary task, dialogue, system credibility, and social supports—drawn from the Persuasive System Design Model. We describe the iterative design of PDVs and a within-subjects preliminary validation to check their persuasive potential. We then assess PDVs’ feasibility using the Persuasive Potential Questionnaire in a between-subjects study comparing a PDV against a conventional DV on Amazon Mechanical Turk (N = 252). Our results indicate the feasibility of using PDVs in providing individuals’ self-tracking feedback to convey persuasive health messages, based on which we discuss opportunities for designing persuasive behavioral feedback in an engaging way.