Nutritional Language Model

NCT06661590 | Completed

• 1 other identifier: STUDY00023189
• Study Type: interventional
• Target: 6
• Locations: 1 country
• Sites: 1

Brief Summary

Colorectal cancer survivors often face unique nutritional challenges and require support in their recovery and long0term health. While human experts have traditionally provided that support, there has been an increase in the use of Large Language Models (LLM) in medicine and in nutrition. The LLM offers a potential supplementary resource for generating personalized nutritional advice, specifically in personalized messaging. However, the efficacy and reliability of these AI-generated messages in comparison to human expert advice remain underexplored specific to this population. This study aims to compare the nutrition-related content generated by popular LLMs-ChatGPT, Claude, Gemini, and Co-Pilot-against messages crafted by human experts. By evaluating the generated content in terms of readability, thematic relevance, medical relevance, perceived effectiveness, and implementation of participants' clinical practice, this research will provide insights into the strengths and limitations of using AI for nutritional guidance in colorectal cancer care.

Conditions

No Disease State or Condition

Outcome Measures

Primary Outcomes (5)

• Outcome Measure Title: Readability of Nutrition Messages

  Description: The readability of AI-generated and human expert-generated nutrition messages will be measured using the Flesch-Kincaid Grade Level tool. Unit of Measure: Grade level score (numerical score indicating reading difficulty level). Measurement Tool: Flesch-Kincaid Grade Level formula. Scale values: The values vary from 0 to 18, where 18 represents the most difficult text.

  8 to 12 months

• Outcome Measure Title: Thematic Relevance of Nutrition Messages

  Description: Thematic relevance of nutrition messages will be assessed by experts in nutrition using a thematic coding framework specifically designed for this study. Unit of Measure: Percentage (%) of messages that align with pre-determined thematic codes relevant to colorectal cancer survivorship. Measurement Tool: Thematic coding framework created by the research team. Scale values: The themes are capability (C), opportunity (O), and motivation (M) as three key factors capable of changing behavior (B).

  8 to 12 months

• Outcome Measure Title: Medical Relevance to Colorectal Cancer Survivors

  Description: Medical relevance will be rated by specialists using a 0-5 relevance rating scale. Unit of Measure: Mean relevance score (0-5). Measurement Tool: Dietitians/Participants review using a relevance rating scale. Scale value: 1-5 (1- least, 5- most)

  8-12 months

• Outcome Measure Title: Perceived Effectiveness of Nutrition Messages

  Description: Perceived effectiveness will be measured using a mean relevance score (1-5) administered to dietitians and participants. Unit of Measure: Mean relevance score (1-5). Measurement Tool: Dietitians/Participants survey. Scale value: 1-5 (1- least, 5- most)

  8-12 months

• Outcome Measure Title: Potential for Implementation in Clinical Practice

  Description: Feasibility for clinical implementation will be rated by dietitians using a 1-5 feasibility scale. Unit of Measure: Mean feasibility score. Measurement Tool: Dietitians/Participants survey. Scale value: 1-5 (1- least, 5- most)

  8-12 months

Study Arms (1)

Dietician

EXPERIMENTAL

Other: Nutritional Messaging

Interventions

Nutritional Messaging OTHER

Dieticians will evaluate nutritional messages created by LLM and Human Experts.

Dietician

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• + years of age
• Currently practicing Registered Dietitian Nutritionist with at least five years of experience working with oncology patients and survivors in their practice.
• Must have access to computer and internet access.

You may not qualify if:

• Non-English speakers, as the study materials and assessments are in English.
• Experts with conflicts of interest related to any of the LLMs that are being evaluated.

Sponsors & Collaborators

• University of Minnesota: lead

Study Sites (1)

The Hormel Institute - University of Minnesota, Medical Research Center

Austin, Minnesota, 55912, United States

Location

Related Publications (1)

• Shah NH, Entwistle D, Pfeffer MA. Creation and Adoption of Large Language Models in Medicine. JAMA. 2023 Sep 5;330(9):866-869. doi: 10.1001/jama.2023.14217.

  PMID: 37548965 BACKGROUND

Related Links

• Related Info

Study Officials

• Annie Lin, RD, PhD

  Hormel Institute

  PRINCIPAL INVESTIGATOR

• Glen Morris, PhD

  Hormel Institute

  STUDY CHAIR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 15, 2024
• First Posted: October 28, 2024
• Study Start: October 15, 2024
• Primary Completion: December 20, 2024
• Study Completion: February 6, 2025
• Last Updated: May 15, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)