With increasing interest in body shape and health management, the mobile healthcare market has been experiencing high growth rates, leading to active research on healthcare apps. Previous studies have shown that current healthcare apps have limitation...
With increasing interest in body shape and health management, the mobile healthcare market has been experiencing high growth rates, leading to active research on healthcare apps. Previous studies have shown that current healthcare apps have limitations in managing and analyzing user-entered data, which necessitates effective methods for managing healthcare app data. In this study, we propose the application of digital twin technology, which involves managing and analyzing data, to healthcare apps, as well as conducting research on user-centered information visualization, an area that has not been extensively explored in healthcare apps.
To understand information visualization and digital twin technology, we conducted a literature review and case study. Through this, we established the hypothesis that information visualization can aid user understanding and effective presentation of data, while digital twin technology, if visualized in healthcare apps, can effectively present data through virtual avatars. Subsequently, we analyzed healthcare apps to determine the need for applying digital twin technology and proposed ways to implement it in current healthcare apps. In the case study of healthcare app information visualization, we categorized human-like characters into five stages: hyper-realistic, realistic, semi-realistic, simplistic, and hyper-simplistic.
Next, through user surveys, we identified areas for improvement in healthcare apps and derived four approaches: "providing body shape changes rather than just weight changes," "showing post-management results rather than just recorded data," "providing predictions of body shape changes upon achieving goals," and "offering personalized management methods and feedback."
Finally, to achieve information visualization in healthcare apps with applied digital twin technology, we developed eleven evaluation tools based on the previously classified five human-like character categories. We conducted user evaluations using the four proposed approaches for applying digital twin technology in healthcare apps. The results showed that when human-like virtual avatars were not used, higher levels of realism correlated with higher levels of trust, while hyper-simplistic avatars received higher likability ratings. When human-like virtual avatars were combined with data graphs, lower levels of trust and likability were observed for higher levels of realism, while higher levels of trust and likability were observed for hyper-simplistic avatars. Additionally, evaluation tools consisting of only data graphs received lower trust and likability scores overall. The evaluation results indicate that to effectively use virtual avatars, healthcare app objectives and types should be considered, and selectively choosing their use is desirable. Furthermore, it was observed that simple representations of human-like virtual avatars have a positive impact on users when accompanied by quantitative information in the form of graphs.
In this study, we identified four approaches for applying digital twin technology to improve the management of data in current healthcare apps. Through user evaluations, we confirmed the effectiveness of virtual avatars in terms of trust and likability for efficient information visualization. The findings of this study can contribute to future research on integrating digital twin technology into healthcare apps and user-centered information visualization, which has yet to be explored extensively.