大型语言模型代理人改善与行为改变干预的参与:应用到数字正念的应用
Harsh Kumar1, Suhyeon Yoo1, Angela Zavaleta Bernuy2
1University of Toronto, Canada.
概括
大型语言模型 (LLM) 可以通过模仿社会对行为改变的支持来支持健康. 提供信息的LLM代理人与友好的人格显著改善了对正念练习的参与.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 行为科学是一种行为科学.
- 医疗保健中的人工智能
背景情况:
- 随着时间的推移,自我指导的健康参与往往会下降.
- 对于行为改变的传统社会支持往往是无法获得的.
- 大型语言模型 (LLM) 显示了模拟社会支持的潜力.
研究的目的:
- 评估LLM代理对用户参与正念练习的影响.
- 探索信息提供与自我反思促进的LLM代理人的有效性.
- 为了调查LLM驱动的社会支持持续的行为变化.
主要方法:
- 进行了两个随机实验.
- 研究1:对502名群众工作者进行一次性研究.
- 研究2:一项为期三周的54名参与者的研究,比较了两种LLM代理类型.
主要成果:
- 这两种LLM代理都增加了用户练习正念的意图.
- 提供信息的LLM代理与友好的人格显著改善了炼参与度.
- 法学士代理商在弥合数字健康的社会支持差距方面表现有前途.
结论:
- 特定的LLM代理设计可以加强对数字健康干预的参与.
- 以LLM为动力的社会支持可能是行为改变的可扩展解决方案.
- 对LLM应用在现场行为改变方面的进一步研究是有必要的.
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