A Feasibility and Acceptability Study of a Large Language Model-based Chatbot for Brief Alcohol Intervention Among Emerging Adults

NCT ID: NCT07214831

Last Updated: 2025-10-22

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 20 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2027-06-01
• Study Completion Date: 2028-08-31

Brief Summary

American emerging adults (EAs; aged 18-29 years) have the highest rates of alcohol use disorder (AUD) and the lowest levels of treatment engagement of any age group. Innovative, scalable, and cost-effective strategies are needed to expand early detection and intervention for EAs engaged in patterns of drinking associated with AUD. Because digital technology use is frequent among EAs, digital interventions may be a particularly suitable way to reach this population. Prior studies of digital alcohol interventions demonstrate modest but consistent reductions in alcohol use, but these tools are often limited by a lack of interactivity and personalization. Large language model (LLM)-based chatbots, such as ChatGPT, may address these limitations by enabling personalized, adaptive, and human-like engagement. These features have the potential to increase uptake and engagement with screening and brief interventions among EAs. This study will develop, validate, and conduct an open trial of an LLM-based chatbot-delivered brief intervention designed to reduce alcohol use and problems among EAs, with the primary goal of establishing preliminary feasibility and acceptability.

Detailed Description

This feasibility and acceptability study will develop, validate, and conduct a Phase I single-arm open trial of a large language model (LLM)-based chatbot-delivered brief intervention designed to reduce alcohol use and problems among EAs. To develop the augmented LLM, the investigators will use instruction fine-tuning to enhance conversational abilities within the context of brief interventions based on high-fidelity recordings of sessions from prior clinical trials and simulated patient-provider interactions. A retrieval augmented generation system will be developed to ensure the model delivers accurate information. The augmented LLM will be incorporated into a chatbot interface delivered through a user-friendly web application. To validate the chatbot's capability for delivering brief alcohol interventions, patient actors (clinical or counseling psychology PhD students) will be assigned clinical vignettes depicting diverse EAs with patterns of drinking associated with alcohol use disorder. Patient actors will engage in two randomly ordered online text-based brief intervention sessions for each vignette (one with the chatbot and one with a human clinician). Blinded transcripts from sessions will be reviewed by experts to assess treatment fidelity. To maximize and test initial feasibility and acceptability of the intervention, the investigators will conduct semi-structured interviews with 20 EAs who report hazardous drinking, followed by an open trial with another 20 EAs.

Conditions

Alcohol Use Disorder

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

Large language model-based chatbot brief alcohol intervention

All participants will interact with a large language model-based chatbot designed to deliver a brief alcohol intervention session.

Group Type: EXPERIMENTAL

Large language model-based chatbot brief alcohol intervention session

Intervention Type: BEHAVIORAL

The intervention is a large language model-based chatbot designed to delivered brief alcohol interventions using motivational interviewing-consistent strategies. The chatbot session will last approximately 45 minutes and will include a decisional balance exercise, feedback on drinking patterns, normative beliefs about drinking, alcohol-related consequences, goal setting, and harm-reduction strategies.

Interventions

Large language model-based chatbot brief alcohol intervention session

The intervention is a large language model-based chatbot designed to delivered brief alcohol interventions using motivational interviewing-consistent strategies. The chatbot session will last approximately 45 minutes and will include a decisional balance exercise, feedback on drinking patterns, normative beliefs about drinking, alcohol-related consequences, goal setting, and harm-reduction strategies.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• 18-29 years old
• Engaged in past-month hazardous drinking (consuming ≥ 5/4 drinks for men/women on two or more occasions in the past month) or exceeded recommended guidelines for risky drinking (> 14/7 drinks per week for men/women)
• Able to read and comprehend English at a 5th grade level

Exclusion Criteria

• Report a history of or active psychosis
• Previous or current engagement in alcohol or drug treatment
• Risk for alcohol withdrawal as evidenced by very heavy weekly drinking reports on the alcohol screener (> 40 standard drinks in a typical week in the past month)
• Demonstrate inability or unwillingness to attend in-person office visits
• Demonstrate inability or unwillingness to identify an emergency contact who could be contacted in case the participant becomes lost to follow-up

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 29 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institute on Alcohol Abuse and Alcoholism (NIAAA)

NIH

Sponsor Role: collaborator

Massachusetts General Hospital

OTHER

Sponsor Role: lead

Responsible Party

Russell, Alex

Assistant Professor of Psychiatry

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Alex M Russell, PhD

Role: PRINCIPAL_INVESTIGATOR

Massachusetts General Hospital

Locations

Massachusetts General Hospital

Boston, Massachusetts, United States

Countries

United States

Central Contacts

Alex M Russell, PhD

Role: CONTACT

arussell11@mgh.harvard.edu

617-724-0924

Samuel F Acuff, PhD

Role: CONTACT

sacuff@mgh.harvard.edu

Facility Contacts

Research Coordinator

Role: primary

617-724-0924

Other Identifiers

R34AA032472

Identifier Type: NIH

Identifier Source: secondary_id

View Link

2025P001459

Identifier Type: -

Identifier Source: org_study_id