RainMind: Investigating Dynamic Natural Soundscape of Physiological Data to Promote Self-Reflection for Stress Management.

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Title: RainMind: Investigating Dynamic Natural Soundscape of Physiological Data to Promote Self-Reflection for Stress Management.
Authors: Yan, Ran (AUTHOR), Ren, Xipei (AUTHOR), Wang, Siming (AUTHOR), Bai, Xinhui (AUTHOR), Zhang, Xiaoyu (AUTHOR)
Source: International Journal of Human-Computer Interaction. May2025, Vol. 41 Issue 9, p5545-5562. 18p.
Subjects: Web-based user interfaces, User experience, Stress management, Task analysis, Introspection
Abstract: Metaphorical auditory displays are increasingly recognized for data presentation and self-reflection. This article presents the design and evaluation of RainMind, a web-based soundscape application that represents physiological data with dynamic natural sounds for daily stress reflection. Based on different combinations of auditory display with visualization, we identified three modes: the Visual-Aided Mode (VAM), the Audio-Aided Mode (AAM), and the Audio-Visual Mode (AVM). Through a within-subject study involving 30 participants, we conducted a mixed-methods evaluation to assess the task load, engagement, and user experience among the three modes. The findings indicated that the combination of dynamic natural soundscapes with visualization (AVM) contributes to a lower task load compared to the other two modes. Moreover, dynamic natural soundscapes as metaphors for stress data significantly enhanced engagement and user experience of self-reflection compared to static natural sounds. Based on our study, we discuss the potential of leveraging dynamic natural soundscapes as a new way of data-driven self-reflection. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: RainMind: Investigating Dynamic Natural Soundscape of Physiological Data to Promote Self-Reflection for Stress Management.
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  Data: <searchLink fieldCode="AR" term="%22Yan%2C+Ran%22">Yan, Ran</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Xipei%22">Ren, Xipei</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Siming%22">Wang, Siming</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bai%2C+Xinhui%22">Bai, Xinhui</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaoyu%22">Zhang, Xiaoyu</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. May2025, Vol. 41 Issue 9, p5545-5562. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Web-based+user+interfaces%22">Web-based user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22User+experience%22">User experience</searchLink><br /><searchLink fieldCode="DE" term="%22Stress+management%22">Stress management</searchLink><br /><searchLink fieldCode="DE" term="%22Task+analysis%22">Task analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Introspection%22">Introspection</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Metaphorical auditory displays are increasingly recognized for data presentation and self-reflection. This article presents the design and evaluation of RainMind, a web-based soundscape application that represents physiological data with dynamic natural sounds for daily stress reflection. Based on different combinations of auditory display with visualization, we identified three modes: the Visual-Aided Mode (VAM), the Audio-Aided Mode (AAM), and the Audio-Visual Mode (AVM). Through a within-subject study involving 30 participants, we conducted a mixed-methods evaluation to assess the task load, engagement, and user experience among the three modes. The findings indicated that the combination of dynamic natural soundscapes with visualization (AVM) contributes to a lower task load compared to the other two modes. Moreover, dynamic natural soundscapes as metaphors for stress data significantly enhanced engagement and user experience of self-reflection compared to static natural sounds. Based on our study, we discuss the potential of leveraging dynamic natural soundscapes as a new way of data-driven self-reflection. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10447318.2024.2364468
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      – Code: eng
        Text: English
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        PageCount: 18
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      – SubjectFull: Web-based user interfaces
        Type: general
      – SubjectFull: User experience
        Type: general
      – SubjectFull: Stress management
        Type: general
      – SubjectFull: Task analysis
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      – SubjectFull: Introspection
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            NameFull: Yan, Ran
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            NameFull: Ren, Xipei
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            NameFull: Wang, Siming
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            – D: 01
              M: 05
              Text: May2025
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              Y: 2025
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