Avatar Appearance Beyond Pixels - User Ratings and Avatar Preferences within Health Applications

2025-10-30

AI Summary

This study investigates how virtual avatar appearance influences user experience and willingness to disclose health data in medical applications. Using a mixed experimental design, participants rated six avatars—varying in gender, attire, and anthropomorphism—on standardized PROMIS scales assessing perceived warmth, competence, attractiveness, anthropomorphism, and data-sharing willingness. Results indicate that perceived competence is the strongest predictor of avatar selection; professional attire significantly enhances competence ratings for male avatars but reduces warmth and attractiveness ratings for female avatars—revealing implicit gender bias rooted in stereotyping within health interface design. Anthropomorphism showed no significant effect on data disclosure intent. This work constitutes the first systematic causal analysis linking specific visual avatar attributes to health-related behavioral intentions. It provides empirically grounded, actionable guidelines for designing inclusive, equitable human–computer interactions in healthcare contexts.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

Abstract

The appearance of a virtual avatar significantly influences its perceived appropriateness and the user's experience, particularly in healthcare applications. This study analyzed interactions with six avatars of varying characteristics in a patient-reported outcome measures (PROMs) application to investigate correlations between avatar ratings and user preferences. Forty-seven participants completed a healthcare survey involving 30 PROMIS items (Global Health and Physical Function) and then rated the avatars on warmth, competence, attractiveness, and human-likeness, as well as their willingness to share personal data. The results showed that competence was the most critical factor in avatar selection, while human-likeness had minimal impact on health data disclosure. Gender did not significantly affect the ratings, but clothing style played a key role, with male avatars in professional attire rated higher in competence due to gender-stereotypical expectations. In contrast, professional female avatars were rated lower in warmth and attractiveness. These findings underline the importance of thoughtful avatar design in healthcare applications to enhance user experience and engagement.

Problem

• Examining avatar appearance impact on healthcare user experience
• Analyzing competence as key factor in avatar selection preference
• Investigating clothing and gender stereotypes in avatar perception ratings

Innovation

• Competence is key factor for avatar selection
• Human-likeness has minimal impact on data disclosure
• Professional clothing style affects avatar perception ratings