{"title":"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","authors":"Yahui Wang, Xuhong Li, Qiaochu Zhang, Dannii Yeung, Yihan Wu","doi":"10.2196/63806","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e63806"},"PeriodicalIF":6.2000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396778/pdf/","citationCount":"0","resultStr":"{\"title\":\"Effect of a Cognitive Behavioral Therapy-Based AI Chatbot on Depression and Loneliness in Chinese University Students: Randomized Controlled Trial With Financial Stress Moderation.\",\"authors\":\"Yahui Wang, Xuhong Li, Qiaochu Zhang, Dannii Yeung, Yihan Wu\",\"doi\":\"10.2196/63806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":14756,\"journal\":{\"name\":\"JMIR mHealth and uHealth\",\"volume\":\"13 \",\"pages\":\"e63806\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2025-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396778/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR mHealth and uHealth\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/63806\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR mHealth and uHealth","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/63806","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Effect of a Cognitive Behavioral Therapy-Based AI Chatbot on Depression and Loneliness in Chinese University Students: Randomized Controlled Trial With Financial Stress Moderation.
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.
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.