Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening
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arXiv
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| Format: | Preprint |
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2025
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| author | Guo, Zhijun Lai, Alvina Ive, Julia Petcu, Alexandru Wang, Yutong Qi, Luyuan Thygesen, Johan H Li, Kezhi |
| author_facet | Guo, Zhijun Lai, Alvina Ive, Julia Petcu, Alexandru Wang, Yutong Qi, Luyuan Thygesen, Johan H Li, Kezhi |
| contents | Static tools like the Patient Health Questionnaire-9 (PHQ-9) effectively screen depression but lack interactivity and adaptability. We developed HopeBot, a chatbot powered by a large language model (LLM) that administers the PHQ-9 using retrieval-augmented generation and real-time clarification. In a within-subject study, 132 adults in the United Kingdom and China completed both self-administered and chatbot versions. Scores demonstrated strong agreement (ICC = 0.91; 45% identical). Among 75 participants providing comparative feedback, 71% reported greater trust in the chatbot, highlighting clearer structure, interpretive guidance, and a supportive tone. Mean ratings (0-10) were 8.4 for comfort, 7.7 for voice clarity, 7.6 for handling sensitive topics, and 7.4 for recommendation helpfulness; the latter varied significantly by employment status and prior mental-health service use (p < 0.05). Overall, 87.1% expressed willingness to reuse or recommend HopeBot. These findings demonstrate voice-based LLM chatbots can feasibly serve as scalable, low-burden adjuncts for routine depression screening. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening Guo, Zhijun Lai, Alvina Ive, Julia Petcu, Alexandru Wang, Yutong Qi, Luyuan Thygesen, Johan H Li, Kezhi Artificial Intelligence Computation and Language Human-Computer Interaction Static tools like the Patient Health Questionnaire-9 (PHQ-9) effectively screen depression but lack interactivity and adaptability. We developed HopeBot, a chatbot powered by a large language model (LLM) that administers the PHQ-9 using retrieval-augmented generation and real-time clarification. In a within-subject study, 132 adults in the United Kingdom and China completed both self-administered and chatbot versions. Scores demonstrated strong agreement (ICC = 0.91; 45% identical). Among 75 participants providing comparative feedback, 71% reported greater trust in the chatbot, highlighting clearer structure, interpretive guidance, and a supportive tone. Mean ratings (0-10) were 8.4 for comfort, 7.7 for voice clarity, 7.6 for handling sensitive topics, and 7.4 for recommendation helpfulness; the latter varied significantly by employment status and prior mental-health service use (p < 0.05). Overall, 87.1% expressed willingness to reuse or recommend HopeBot. These findings demonstrate voice-based LLM chatbots can feasibly serve as scalable, low-burden adjuncts for routine depression screening. |
| title | Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.05984 |