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arXiv 2609.15867cs.HC

牙科椅上的边缘时刻:为牙科焦虑时刻设计VR支持

On Edge in the Dental Chair: Designing VR Support for Moments of Dental Anxiety

Zhu Guo, Junjie Zhao, Haofan He, Jiaming Zhang, Mingshi Deng, Mingjun Zhou, Dongyijie Primo Pan, Zikun Jin, Jianquan Li, Liangyi Chen, Zuolin Jin, Benyou Wang, … 展开作者

Zhu Guo, Junjie Zhao, Haofan He, Jiaming Zhang, Mingshi Deng, Mingjun Zhou, Dongyijie Primo Pan, Zikun Jin, Jianquan Li, Liangyi Chen, Zuolin Jin, Benyou Wang, Jie Li, Siying Hu, Shan Jiang, Junwen Wang

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中文总结 AI 辅助

本研究针对牙科焦虑随治疗过程变化的问题,提出基于事件的VR干预框架,通过随机实验(N=24)显示干预显著降低焦虑评分,为模拟牙科VR中的及时支持提供依据。

中文摘要 AI 辅助

牙科焦虑会随着治疗过程的展开而变化,然而牙科虚拟现实(VR)通常提供持续的分散注意力或放松。我们研究了如何将支持与特定的模拟牙科事件相协调。利益相关者访谈(N=36)、与三位复诊牙医的参与式设计,以及无干预原型的患者走查(N=12)共同确定了五个焦虑事件和一个干预模块框架。基于认知脆弱性和情绪调节理论,我们实现了一个标准化的基于事件的VR系统,该系统具有预定义的事件-模块分配以及共享代理和安全控制。一项随机研究(N=24)将干预包与无干预VR进行了比较。调整后的干预组减去对照组差异在各事件中平均为-12.83 VAS-A分(95%置信区间[-24.42, -1.70])。生理、行为和定性测量为参与者的体验提供了背景信息。研究结果为模拟牙科VR中及时、可理解的支持和令人安心的社会存在提供了信息;这些发现涉及完整干预包,而非单个模块或临床有效性。

英文摘要

Dental anxiety can change as a procedure unfolds, yet dental virtual reality (VR) commonly provides continuous distraction or relaxation. We investigate how support can be coordinated with specific simulated dental events. Stakeholder interviews (N=36), participatory design with three returning dentists, and patient walkthroughs of a no-intervention prototype (N=12) informed five Anxiety Events and an intervention-module framework. Drawing on cognitive vulnerability and emotion regulation, we implemented a standardized event-contingent VR system with predefined event-module assignments and shared agency and safety controls. A randomized study (N=24) compared the intervention package with no-intervention VR. The adjusted intervention-minus-control difference averaged -12.83 VAS-A points across events (95% CI [-24.42, -1.70]). Physiological, behavioural, and qualitative measures contextualized participants' experiences. The findings inform timely, comprehensible support and reassuring social presence in simulated dental VR; they concern the complete package rather than individual modules or clinical effectiveness.

发表机构

  • School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen(香港中文大学深圳人工智能学院)
  • Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong(香港大学牙医学院应用口腔科学与社区牙科护理部)
  • Beijing Institute of Collaborative Innovation(北京协同创新研究院)
  • School of Design, Hunan University(湖南大学设计学院)
  • Shenzhen Clinical College of Stomatology, School of Stomatology, Southern Medical University(南方医科大学口腔医学院深圳临床学院)
  • Department of Periodontology, Shenzhen Stomatology Hospital (Pingshan) of Southern Medical University(南方医科大学深圳口腔医院(坪山)牙周科)
  • School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen(香港中文大学深圳科学与工程学院)
  • School of Data Science, The Chinese University of Hong Kong, Shenzhen(香港中文大学深圳数据科学学院)
  • Computational Media and Arts, The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州)计算媒体与艺术)

机构由 AI 辅助整理,请以论文原文为准。

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