在行为心理学中微调大型语言模型,以实现可扩展的体力活动辅导
Sriya Mantena1, Anders Johnson1, Marily Oppezzo2,3
1Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
medRxiv : the preprint server for health sciences
|March 4, 2025
概括
一个新的AI教练,MHC-Coach,提供个性化的信息来鼓励身体活动. 用户更喜欢人工智能信息而不是人类信息,显示出长期行为改变和降低心血管疾病风险的潜力.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 人工智能的人工智能
背景情况:
- 基于智能手机的辅导可以增强身体活动,但通常使用静态的,非个性化的信息.
- 行为科学框架,如超理论变化模型,为行为修改提供理论基础.
研究的目的:
- 引入和评估MHC-Coach,这是一个基于大型语言模型的系统,用于个性化健康指导.
- 评估人工智能产生的心理定制信息在促进长期体力活动中的有效性.
主要方法:
- 通过对跨理论变化模型进行微调,开发了MHC-Coach大型语言模型.
- 对632名参与者进行了一项研究,将MHC-Coach信息与促进体育活动的人类专家信息进行比较.
- 收集了参与者的偏好数据和专家对消息有效性和理论一致性的评价.
主要成果:
- 68.0%的参与者在与他们的变化阶段相匹配时更喜欢MHC-Coach生成的消息 (P < 0.001).
- 盲人专家对MHC-Coach信息的感知有效性 (4.4对2.8) 和超理论模型对齐 (4.1对3.5) 的评价更高.
结论:
- 大型语言模型可以有效地运行行为科学框架,以提供个性化的健康指导.
- MHC-Coach证明了促进持续体育活动行为改变的潜力.
- 这种方法可能有助于通过可访问的数字干预措施大规模减少心血管疾病风险.
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