Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
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.
