在同行互动中排序对话转换:基于证据的对话代理的综合方法,用于及时的尼古丁渴望干预
Tavleen Singh1, Michael Truong1, Kirk Roberts1
1McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, Texas, USA.
这项研究介绍了一种人工智能驱动的对话代理,用于预防戒烟复发. 它使用社交媒体数据和行为理论来创建个性化的干预措施,改善对行为改变的支持.
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:
- 有风险的健康行为给公共卫生系统带来了压力.
- 目前的复发预防方法显示出不理想的结果.
- 人工智能 (AI) 为适应性行为变化支持提供了新的解决方案.
研究的目的:
- 开发一个对话代理 (CA) 用于戒烟的复发预防.
- 将行为理论与社交媒体数据集成为个性化干预.
- 为了提高行为改变支持系统的有效性.
主要方法:
- 模拟的同行交互数据 (n=1000) 使用行为改变技术 (BCT) 和语音行为 (SA) 理论.
- 在社交对话中分析了BCT和SA的顺序模式 (n=339,067).
- 利用接地理论提取用户需求并将其映射到CA架构中.
主要成果:
- 经常发生的顺序模式包括BCT的"行为比较"和"反和监测".
- "指令"和"断言"是SAS的常见顺序模式.
- 确定了五种与渴望相关的场景及其相关的行为变化构造.
结论:
- 由人工智能主导的虚拟CA需要数据驱动,理论相关的方法来实现参与和可持续性.
- 考虑理论和意图的顺序模式对于有效的行为改变CA至关重要.
- 这一框架为个性化复发预防提供了一种新的方法.
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