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Persuasive Data Videos: Investigating Persuasive Self-Tracking Feedback with Augmented Data Videos

Abstract : 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.
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Submitted on : Monday, May 4, 2020 - 9:47:55 AM
Last modification on : Wednesday, November 3, 2021 - 8:11:33 AM


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  • HAL Id : hal-02561809, version 1



Eun Choe, Yumiko Sakamoto, Yanis Fatmi, Bongshin Lee, Christophe Hurter, et al.. Persuasive Data Videos: Investigating Persuasive Self-Tracking Feedback with Augmented Data Videos. AMIA 2019, Annual Symposium Informatics: From Data to Knowledge to Action, Nov 2019, Washington, United States. ⟨hal-02561809⟩



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