No Disease State or ConditionNLMNCT06661590
Nutritional Language Model
A study testing Nutritional Messaging in people with No Disease State or Condition, run by University of Minnesota at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- No Disease State or Condition
- What is being tested?
- Nutritional Messaging
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 6 participants
- Where
- 1 siteUnited States
- Study dates
- 15 Oct 2024to 6 Feb 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
United States1 site
The Hormel Institute - University of Minnesota, Medical Research Center
Austin, Minnesota, 55912
About this study
Official title: Comparative Analysis Between Artificial Intelligence vs. Human Generated Nutrition Messages for Colorectal Cancer Survivors
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
- Being tested
- Nutritional Messaging
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Nutritional MessagingOther
Dieticians will evaluate nutritional messages created by LLM and Human Experts.
| Study group | Role | What they receive |
|---|---|---|
| Dietician | Experimental | Not described Receives: Other: Nutritional Messaging |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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).
Measured: 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)
Measured: 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)
Measured: 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)
Measured: 8-12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Oct 2024
- Study start15 Oct 2024
- Primary completion20 Dec 2024
- Study completion6 Feb 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for No Disease State or ConditionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 6 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Minnesota
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Annie Lin, RD, PhD, Principal investigator, Hormel InstituteGlen Morris, PhD, Study chair, Hormel Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06661590
- Sponsor's protocol ID
- STUDY00023189
- Acronym
- NLM
- Status last verified
- May 2025
- First submitted
- 15 Oct 2024
- Last updated
- 15 May 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: We do not plan on sharing the list of participation. This is due to the expected low enrollment amount of 6 participants.
Publications
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.
PubMed 37548965
Study at a glance
From the registry record, last updated by the sponsor on 15 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparative Analysis Between Artificial Intelligence vs. Human Generated Nutrition Messages for Colorectal Cancer Survivors
- ClinicalTrials.gov ID
- NCT06661590
- Acronym / sponsor ID
- NLM / STUDY00023189
- Conditions
- No Disease State or Condition
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 6 participants (actual)
- Lead sponsor
- University of MinnesotaUniversity, hospital or other
- Study start
- 15 Oct 2024 (actual)
- Primary completion
- 20 Dec 2024 (actual)
- Study completion
- 6 Feb 2025 (actual)
- First posted
- 28 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Dietician | Experimental | Other: Nutritional Messaging |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Nutritional MessagingDieticians will evaluate nutritional messages created by LLM and Human Experts. | Other | Dietician |
Primary outcome
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.
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).
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)
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)
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)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 May 2025.
- Background: PubMed 37548965Shah 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.
- Related InfoLink provided by the sponsor.
- Individual participant dataSharing planned: No. We do not plan on sharing the list of participation. This is due to the expected low enrollment amount of 6 participants.
Related trials for No Disease State or Condition
RecruitingObservational study
Dozee Validation for Contactless Heart Rate and Respiration Monitoring in Clinical Settings
No Specific Medical Conditions or Disease States
Not yet recruiting
RCT of HeartBot in Women
Participants Must be Women Aged 25 Years or Older, Participants Should Have no Self-reported History of Heart Disease or Stroke
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Annie Lin, RD, PhD principal investigator
Hormel Institute
Glen Morris, PhD study chair
Hormel Institute
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Nutritional Messaging in people with No Disease State or Condition, run by University of Minnesota at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Outcome Measure Title: Readability of Nutrition Messages (time frame: 8 to 12 months). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06661590, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 May 2025.