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Factors influencing people’s emotional experiences when using ChatGPT for health information: a cross-sectional web-based questionnaire survey in the UK

Understanding how individuals emotionally respond to AI-driven chatbots in healthcare is crucial for optimising user experience and ensuring effective health information delivery. This study aimed to investigate the factors influencing people’s emotional experiences when using ChatGPT for health information. A cross-sectional, web-based questionnaire survey was conducted in August 2024, targeting a demographically representative sample in the UK. A total of 462 respondents participated. Data was analysed descriptively and using a multiple stepwise linear regression analysis to explore the relationship between people’s emotional responses, measured using the PANAS tool, and user characteristics. The independent variables included (1) sociodemographics, (2) general (non-health-related) information-seeking behaviours towards the use of ChatGPT, and (3) health-related information-seeking behaviours using ChatGPT (including perceived aesthetics, usability, and level of trust towards ChatGPT). The results showed that older users (35+ years) and ethnic minorities (Asian or Black participants) reported higher positive emotions, while younger users (25–34 years) experienced more negative emotions when using ChatGPT for health information. Positive emotions were strongly associated with confidence in using ChatGPT, perceived trust, and perceived aesthetics, while negative emotions were linked to longer conversations, lower perceived trust, and poor functional quality. These findings highlighted the need to improve ChatGPT’s aesthetics, usability, trustworthiness, and integration to enhance users’ positive emotions in health information-seeking contexts.<p></p>

Funding

Loughborough University

China Scholarship Council

History

School

  • Design and Creative Arts

Department

  • Design

Published in

Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. 16th International Conference, DHM 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, June 22–27, 2025, Proceedings, Part III. Lecture Notes in Computer Science

Volume

15793 LNCS

Pages

332 - 349

Publisher

Springer, Cham

Version

  • AM (Accepted Manuscript)

Rights holder

© The Author(s), under exclusive license to Springer Nature Switzerland AG

Publisher statement

This version of the chapter has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-93508-4_24

Publication date

2025-05-25

Copyright date

2025

ISBN

978303935077; 9783031935084

ISSN

0302-9743

eISSN

1611-3349

Book series

Lecture Notes in Computer Science

Language

  • en

Editor(s)

Vincent G. Duffy

Depositor

Prof Andrew Morris. Deposit date: 13 October 2025

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