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Neural Conversational Agent for Weight Loss Counseling: Protocol for an Implementation and Feasibility Study
Alexander Kotov1, April Idalski Carcone2, Elizabeth Towner2
1Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI, United States.
JMIR Research Protocols
|September 20, 2024
Summary
This study explores the feasibility of using a large language model (LLM) called NAOMI for motivational interviewing (MI) in obesity counseling. If effective, AI counselors could offer a scalable solution for weight management support.
Area of Science:
- Artificial Intelligence in Healthcare
- Behavioral Science
- Digital Health
Background:
- Obesity is a significant chronic disease requiring effective, scalable interventions.
- Current guidelines emphasize lifestyle modification support, often using motivational interviewing (MI).
- Barriers to MI adoption include high training costs and limited provider availability, which AI can address.
Purpose of the Study:
- To assess the feasibility, acceptability, and usability of NAOMI, an LLM-based web app for MI-consistent obesity counseling.
- To evaluate NAOMI's impact on motivational precursors for lifestyle behavior change in patients with overweight and obesity.
Main Methods:
- NAOMI development involves deep learning, few-shot learning, and domain adaptation using psychotherapy transcripts.
- A mixed-methods approach will evaluate 4 NAOMI versions with 10 adults (BMI 25.0–39.9).
- Fidelity to MI, participant perceptions, usability, empathy, and motivational precursors will be assessed.
Main Results:
- NAOMI (version 1.0) has been developed.
- Participant recruitment begins September 2024, with data collection concluding May 2025.
Conclusions:
- LLM-based AI counselors show promise as a cost-effective public health tool for obesity.
- Successful implementation could transform MI delivery, broadening access to psychotherapeutic support.
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