Drug and alcohol dependence · 2025
Development and preliminary testing of a secure large language model-based chatbot for brief alcohol counseling in young adults.
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Suffoletto Brian, Clark Duncan B, Lee Christine, Mason Michael, Schultz Jordan, Szeto Irvin, Walker Denise
Аннотация
OBJECTIVE: Young adults face elevated risks from alcohol use yet encounter significant barriers to accessing evidence-based interventions. Large language models (LLMs) represent a promising advancement for delivering personalized behavioral interventions, but their application to alcohol counseling remains unexplored. This study evaluated the development and preliminary outcomes of a Secure GPT-4-powered text-based Motivational Interviewing Conversational Agent (MICA).
METHOD: Using a prospective single-arm pilot design, we evaluated MICA across two phases (Phase I: n = 8; Phase II: n = 37), editing the LLM prompts between Phases. Participants aged 18-25 who reported consuming ≥ 10 standard alcohol units weekly completed a counseling session with MICA. We evaluated safety and compared MI fidelity (relational and technical sub-scales of the Client Evaluation of MI [CEMI]) and usability (System Usability Scale) between Phases. We also explored surrogate measures of effectiveness (i.e. proportion of change talk to sustain talk from session logs) and qualitative feedback themes.
RESULTS: No unsafe responses were observed. MI fidelity improved significantly in the CEMI relational sub-scale from Phase I to II (67.2 % to 82.6 %, p = 0.03). Usability remained consistently high across phases (Phase I: 85.4; Phase II: 80.9; p = 0.45). The proportion of within-session change talk was also consistently high (Phase I: 65.2 %; Phase II: 75.8 %; p = 0.10).
CONCLUSIONS: This study provides preliminary evidence that LLM-based chatbots can deliver MI-adherent alcohol interventions that are both acceptable to young adults and maintain high MI fidelity. Future research should employ randomized controlled designs with longer follow-up periods to evaluate impact on drinking outcomes.
METHOD: Using a prospective single-arm pilot design, we evaluated MICA across two phases (Phase I: n = 8; Phase II: n = 37), editing the LLM prompts between Phases. Participants aged 18-25 who reported consuming ≥ 10 standard alcohol units weekly completed a counseling session with MICA. We evaluated safety and compared MI fidelity (relational and technical sub-scales of the Client Evaluation of MI [CEMI]) and usability (System Usability Scale) between Phases. We also explored surrogate measures of effectiveness (i.e. proportion of change talk to sustain talk from session logs) and qualitative feedback themes.
RESULTS: No unsafe responses were observed. MI fidelity improved significantly in the CEMI relational sub-scale from Phase I to II (67.2 % to 82.6 %, p = 0.03). Usability remained consistently high across phases (Phase I: 85.4; Phase II: 80.9; p = 0.45). The proportion of within-session change talk was also consistently high (Phase I: 65.2 %; Phase II: 75.8 %; p = 0.10).
CONCLUSIONS: This study provides preliminary evidence that LLM-based chatbots can deliver MI-adherent alcohol interventions that are both acceptable to young adults and maintain high MI fidelity. Future research should employ randomized controlled designs with longer follow-up periods to evaluate impact on drinking outcomes.