Effect of an AI agent trained on a large language model (LLM) as an intervention for depression and anxiety symptoms in young adults: A 28‐day randomized controlled trial

焦虑 随机对照试验 干预(咨询) 对话框 心理干预 萧条(经济学) 临床心理学 心理学 对话系统 年轻人 精神科 医学 发展心理学 内科学 万维网 宏观经济学 经济 计算机科学
作者
Yuqing Zhao,Wei Qian,Ya-Ru Chen,Dong‐Hong Wu,Yujia Luo,Cong Gao,Kankan Wu,Zhengkui Liu
出处
期刊:Applied Psychology: Health and Well-being [Wiley]
卷期号:17 (5): e70067-e70067
标识
DOI:10.1111/aphw.70067
摘要

Abstract Background Young adults face emotional problems in their daily lives. Considering that youth are prevalent among mobile internet users, it would be helpful if functions that can intervene in young people's depression and anxiety can be designed based on short video apps. Large language model (LLM)‐based AI conversational agents based on short video apps may play an important role in intervening in young adults' negative emotions. Methods This study is a 28‐day randomized controlled trial (RCT) in which 865 participants were randomly assigned to an intervention group or a waiting group, and each user was asked to engage in a total of 28 days of dialog intervention with the AI agent and complete three psychological questionnaires. Results The dialog intervention significantly reduced depression in the intervention group at two weeks and significantly reduced both depression and anxiety in the intervention group at four weeks. Conclusions This study found evidence that the LLM‐based conversational agent could effectively alleviate the mild anxiety and depressive symptoms of young adults with negative emotions through dialog interventions when the AI companion bot is used sufficiently enough. Registration Clinicaltrials.gov NCT06346496, https://clinicaltrials.gov/study/NCT06346496 .
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