Effect of a Cognitive Behavioral Therapy-Based AI Chatbot on Depression and Loneliness in Chinese University Students: Randomized Controlled Trial With Financial Stress Moderation.

IF 6.2 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Yahui Wang, Xuhong Li, Qiaochu Zhang, Dannii Yeung, Yihan Wu
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引用次数: 0

Abstract

Background: Mental health concerns are prevalent among university students, with financial stress further compounding these issues. While cognitive behavioral therapy (CBT) is effective for these conditions, its delivery through artificial intelligence (AI) chatbots represents a promising approach, especially in non-Western contexts.

Objective: This study aims to investigate the efficacy of a culturally adapted, CBT-based AI chatbot for improving the well-being of Chinese university students and to examine whether financial stress moderates its effectiveness.

Methods: In this randomized controlled trial, 100 university students (mean age 20.8, SD 2.2 years; 62/100, 62% female) were allocated to either an intervention (n=50) or a waitlist control group (n=50). The intervention group interacted with a CBT-based AI chatbot for 7 consecutive days. Depression (Center for Epidemiologic Studies Depression Scale), anxiety (Generalized Anxiety Disorder-7 scale), and loneliness (UCLA Loneliness Scale) were assessed at baseline, day 3, and day 7. Financial stress was measured using the Psychological Inventory of Financial Scarcity.

Results: Significant group×time interactions were found for depression (F2,196=8.63; P<.001; η²p=.08) and loneliness (F2,196=5.57; P=.004; η²p=.05), but not for anxiety (F2,196=1.31; P=.27; η²p=.01). Post hoc comparisons showed significant reductions in both depression (t=3.85; P<.001) and loneliness (t=4.28; P<.001) from baseline to postintervention in the intervention group, with corresponding effect sizes of Cohen d=0.71 (95% CI 0.30-1.12) and Cohen d=0.60 (95% CI 0.20-1.00), respectively. No significant changes were observed in the waitlist control group. Exploratory subgroup analyses revealed that participants with high financial stress demonstrated significantly greater improvements in depression (F2,52=11.56; P<.001; η²p=.31) and loneliness (F2,52=11.18; P<.001; η²p=.30) compared to those with low financial stress.

Conclusions: The culturally adapted, CBT-based AI chatbot effectively reduced depression and loneliness in Chinese university students, with stronger effects among those experiencing high financial stress. These findings highlight the potential of AI-driven interventions to provide accessible mental health support, particularly for financially stressed students.

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基于认知行为疗法的AI聊天机器人对中国大学生抑郁和孤独感的影响:经济压力调节的随机对照试验
背景:心理健康问题在大学生中普遍存在,经济压力进一步加剧了这些问题。虽然认知行为疗法(CBT)对这些疾病有效,但通过人工智能(AI)聊天机器人进行治疗代表了一种很有前途的方法,尤其是在非西方环境中。目的:本研究旨在调查一种文化适应性的、基于cbt的人工智能聊天机器人对改善中国大学生幸福感的功效,并研究经济压力是否会调节其有效性。方法:在本随机对照试验中,100名大学生(平均年龄20.8岁,SD 2.2岁;62/100,62%女性)被分为干预组(n=50)和候补对照组(n=50)。干预组与基于cbt的AI聊天机器人进行连续7天的互动。抑郁(流行病学研究中心抑郁量表)、焦虑(广泛性焦虑障碍-7量表)和孤独(加州大学洛杉矶分校孤独量表)分别在基线、第3天和第7天进行评估。财务压力是用财务稀缺心理量表来测量的。结论:具有文化适应性、基于cbt的AI聊天机器人有效降低了中国大学生的抑郁和孤独感,在经济压力较大的大学生中效果更明显。这些发现强调了人工智能驱动的干预措施在提供可获得的心理健康支持方面的潜力,特别是对经济压力大的学生。
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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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