Comparative Analysis Between Artificial Intelligence vs. Human Generated Nutrition Messages for Colorectal Cancer Survivors
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 6
- 试验地点
- 2
- 主要终点
- Outcome Measure Title: Perceived Effectiveness of Nutrition Messages
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Health Services Research
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Outcome Measure Title: Perceived Effectiveness of Nutrition Messages
时间窗: 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: Readability of Nutrition Messages
时间窗: 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: Potential for Implementation in Clinical Practice
时间窗: 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)
Outcome Measure Title: Thematic Relevance of Nutrition Messages
时间窗: 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
时间窗: 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)
次要结局
未报告次要终点