Algorithmic Health Advice and Trust Formation: A Phenomenological Study of AI-Based Wellness Platforms

Abstract

The rapid expansion of AI-based wellness platforms has transformed the way health advice is generated, delivered, and trusted. Algorithmic health advice increasingly replaces or supplements human expertise in guiding lifestyle, fitness, and mental well-being decisions, making trust a critical condition for user adoption and sustained engagement. However, existing studies predominantly approach trust from quantitative perspectives, emphasizing usability, acceptance, or compliance, while offering limited insight into users’ lived experiences. This study employs a qualitative phenomenological approach to explore how users experience algorithmic health advice and how trust is formed in interactions with AI-based wellness platforms. Data were collected through in-depth interviews with active users and analyzed using phenomenologically informed thematic analysis. The findings reveal that trust emerges through repeated experiential validation, perceived personalization, and emotional resonance rather than through understanding of algorithmic mechanisms. Trust is shown to be situational, ambivalent, and often outcome-based, persisting despite algorithmic opacity.The study further identifies ethical tensions arising from the normalization of trust without understanding, where algorithmic advice subtly shapes users’ perceptions, behaviors, and self-governance. These findings highlight the need to reconceptualize trust in digital health as a relational and experiential phenomenon rather than a purely technical attribute. The study contributes to digital health and human–AI interaction literature by providing a phenomenological account of trust formation and offering implications for the ethical design of AI-based wellness platforms.

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