Algorithmic Health Advice and Trust Formation: A Phenomenological Study of AI-Based Wellness Platforms
DOI:
https://doi.org/10.62872/o.v3i1.480Keywords:
algorithmic trust, digital health, phenomenology, wellness platformsAbstract
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.
References
Ayemowa, M., Ibrahim, R., & Khan, M. (2024). Analysis of Recommender System Using Generative Artificial Intelligence: A Systematic Literature Review. IEEE Access, 12, 87742-87766. https://doi.org/10.1109/access.2024.3416962.
Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). SAGE Publications.
Degteva, E., & Kuksova, O. (2023). Artificial Intelligent Systems and the Problem of “Natural” Trust. Journal of Digital Economy Research. https://doi.org/10.24833/14511791-2023-1-109-136.
Drach, I., Petroye, O., Borodiyenko, O., Reheilo, I., Bazeliuk, O., Bazeliuk, N., & Slobodianiuk, O. (2023). The Use of Artificial Intelligence in Higher Education. International Scientific Journal of Universities and Leadership. https://doi.org/10.31874/2520-6702-2023-15-66-82.
Hong, S., Fu, S., Șerban, O., Bao, B., Kinross, J., Toni, F., Martin, G., & Vaghela, U. (2025). Safeguarding Patient Trust in the Age of AI: Tackling Health Misinformation with Explainable AI. ArXiv, abs/2509.04052. https://doi.org/10.48550/arxiv.2509.04052.
Hutsaliuk, O., & Mirzoiev, D. (2025). Formation of digital marketing tools in modern conditions. Actual problems of innovative economy and law. https://doi.org/10.36887/2524-0455-2025-1-12.
Marko, J., Neagu, D., & Anand, P. (2025). Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review. BMC Medical Informatics and Decision Making, 25. https://doi.org/10.1186/s12911-025-02884-1.
Mendel, T., Nov, O., & Wiesenfeld, B. (2024). Advice from a Doctor or AI? Understanding Willingness to Disclose Information Through Remote Patient Monitoring to Receive Health Advice. Proceedings of the ACM on Human-Computer Interaction, 8, 1 - 34. https://doi.org/10.1145/3686925.
Misheva, B., Osterrieder, J., Hirsa, A., Kulkarni, O., & Lin, S. (2021). Explainable AI in Credit Risk Management. ArXiv, abs/2103.00949. https://doi.org/10.2139/ssrn.3795322.
Nadeem, A., Marjanovic, O., & Abedin, B. (2022). Gender bias in AI-based decision-making systems: a systematic literature review. Australas. J. Inf. Syst., 26. https://doi.org/10.3127/ajis.v26i0.3835.
Nasirahmadi, A., & Hensel, O. (2022). Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm. Sensors (Basel, Switzerland), 22. https://doi.org/10.3390/s22020498.
Neumann, O., Guirguis, K., & Steiner, R. (2022). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26, 114 - 141. https://doi.org/10.1080/14719037.2022.2048685.
Nevska, A., Pohosian, O., Goncharuk, K., Sofyna, D., Chernenko, O., Tronko, K., Kozhan, N., & Korol, A. (2024). Detecting diabetic retinopathy using an artificial intelligence-based software platform: a pilot study. Oftalmologicheskii Zhurnal. https://doi.org/10.31288/oftalmolzh202412731.
Nov, O., Singh, N., & Mann, D. (2023). Putting ChatGPT’s Medical Advice to the (Turing) Test: Survey Study. JMIR Medical Education, 9. https://doi.org/10.2196/46939.
Olabanji, S., Olaniyi, O., Adigwe, C., Okunleye, O., & Oladoyinbo, T. (2024). AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems. Asian Journal of Research in Computer Science. https://doi.org/10.9734/ajrcos/2024/v17i3423.
Olawade, D., Wada, O., Odetayo, A., David-Olawade, A., Asaolu, F., & Eberhardt, J. (2024). Enhancing Mental Health with Artificial Intelligence: Current Trends and Future Prospects. Journal of Medicine, Surgery, and Public Health. https://doi.org/10.1016/j.glmedi.2024.100099.
Opesemowo, O. (2024). Artificial Intelligence in Education, Bridging Community Gap: A Phenomenological Approach. International Journal of New Education. https://doi.org/10.24310/ijne.14.2024.20505.
Oyekunle, D., Matthew, U., Preston, D., & Boohene, D. (2024). Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects. Journal of Computer and Communications. https://doi.org/10.4236/jcc.2024.126006.
Perski, O., & Short, C. (2021). Acceptability of digital health interventions: embracing the complexity. Translational Behavioral Medicine, 11, 1473 - 1480. https://doi.org/10.1093/tbm/ibab048.
Sohaib, O. (2021). Social Networking Services and Social Trust in Social Commerce: A PLS-SEM Approach. J. Glob. Inf. Manag., 29, 23-44. https://doi.org/10.4018/jgim.2021030102.
Tolu-Akinnawo, O., Ezekwueme, F., Omolayo, O., Batheja, S., & Awoyemi, T. (2025). Advancements in Artificial Intelligence in Noninvasive Cardiac Imaging: A Comprehensive Review. Clinical Cardiology, 48. https://doi.org/10.1002/clc.70087.
Tun, H., Rahman, H., Naing, L., & Malik, O. (2024). Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review. Journal of Medical Internet Research, 27. https://doi.org/10.2196/69678.
Tunkevichus, O., & Bagrationi, K. (2025). People and Machines or People Against Machines? How Readiness to Artificial Intelligence is Changing Higher Education: A Bibliometric Analysis. European Conference on Innovation and Entrepreneurship. https://doi.org/10.34190/ecie.20.1.3816.
Zhulikov, S., & Zhulikova, O. (2025). Assessment of the Achievements of States in the Field of Artificial Intelligence in the Context of International Ratings. Russia & World: Sc. Dialogue. https://doi.org/10.53658/rw2025-4-3(17)-171-187.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Eko Perdana Putra, Inaya Nur Aini (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
