Evidence map›Paper›PMID 40882177›Full record

Trial reportJMIR mHealth and uHealth2025

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

Yahui Wang, Xuhong Li, Qiaochu Zhang, Dannii Yeung, Yihan Wu

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR mHealth and uHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yahui WangDepartment of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China.ORCID 0000-0001-9354-8507
Xuhong LiDepartment of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China.ORCID 0000-0002-9293-0904
Qiaochu ZhangDepartment of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China.ORCID 0000-0003-1187-2464
Dannii YeungDepartment of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China.ORCID 0000-0002-0548-0328
Yihan WuDepartment of Sociology and Social Security, Ginling College, Nanjing Normal University, 1 Wenyuan Rd, Qixia District, Nanjing, Jiangsu, 210000, China, 86 13736333980.ORCID 0000-0003-1697-6019

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Cognitive Behavioral TherapyDepressionFinancial StressLonelinessStudentsAdolescentAdultChinaFemaleGenerative Artificial IntelligenceHumansMaleUniversitiesYoung Adultartificial intelligencechatbotcognitive behavioral therapydepressionfinancial stresslonelinessrandomized controlled trialuniversity students

Identifiers

PMID40882177
PMCPMC12396778

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.