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Completed

NCT Number: NCT06661590

Nutritional Language Model

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.

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Key information

Conditions

No Disease State or Condition

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

The Hormel Institute - University of Minnesota, Medical Research Center

Austin, Minnesota, 55912, United States

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 18+ 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.

Exclusion criteria

  • 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.

Treatment and study plan

Nutritional Messaging

Other

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

Primary outcomes

  1. Outcome Measure Title: Readability of Nutrition Messages

    Time frame: 8 to 12 months

    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.

  2. Outcome Measure Title: Thematic Relevance of Nutrition Messages

    Time frame: 8 to 12 months

    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).

  3. Outcome Measure Title: Medical Relevance to Colorectal Cancer Survivors

    Time frame: 8-12 months

    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)

  4. Outcome Measure Title: Perceived Effectiveness of Nutrition Messages

    Time frame: 8-12 months

    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)

  5. Outcome Measure Title: Potential for Implementation in Clinical Practice

    Time frame: 8-12 months

    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)

Sponsors and collaborators

Lead sponsor

University of Minnesota

Other

Registry information

Official study title

Comparative Analysis Between Artificial Intelligence vs. Human Generated Nutrition Messages for Colorectal Cancer Survivors

Acronym: NLM

Important dates

Study start
2024
Primary completion
2024
Study completion
2025
First posted
Oct 28, 2024
Registry last updated
May 15, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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