Diabetes Prevention AI: Enhancing Prevention Strategies Using Personalized Nutrition and Fitness Coaching for the Next Generation
This study is testing an artificial intelligence (AI) chatbot designed to help adults at risk for type 2 diabetes adopt healthier lifestyles. The chatbot provides personalized and culturally tailored guidance on nutrition, physical activity, resistance training, and goal setting. About 30 adults aged 18 to 55 years will use the chatbot for 6 weeks and provide feedback through surveys and interviews.
Researchers will evaluate whether the chatbot is easy to use, helpful, and trustworthy, and whether data about changes in health behaviors can be feasibly collected, in order to inform larger future studies to improve diabetes prevention.
研究概览
详细说明
Type 2 diabetes continues to be a major public health concern, with growing evidence that individuals with normal body weight may also be at increased risk of developing prediabetes and type 2 diabetes. Existing diabetes prevention programs have primarily targeted individuals with overweight or obesity, creating a need for innovative prevention approaches tailored to younger, normal-weight, and culturally diverse populations.
This study will evaluate a novel artificial intelligence (AI)-powered chatbot designed to deliver personalized, culturally tailored diabetes prevention support. The intervention combines evidence-based nutrition and physical activity guidance with conversational AI technology to provide accessible lifestyle coaching focused on diabetes risk reduction. The chatbot incorporates expert-validated educational content and behavioral support strategies intended to promote healthy habits and sustained engagement.
The chatbot was developed using a curated knowledge base of culturally adapted diabetes prevention recommendations. Personalized guidance is generated using participant preferences, context, and validated educational resources to support lifestyle behaviors associated with improved metabolic health. The platform is intended to provide scalable, user-centered support while maintaining alignment with established diabetes prevention principles.
This pilot study will assess the feasibility and acceptability of implementing an AI-enabled diabetes prevention intervention in a real-world setting. Researchers will evaluate user experiences with the chatbot, including engagement, satisfaction, trust, usability, and perceptions of cultural relevance. Findings will inform future development of digital health interventions and support the design of larger studies aimed at evaluating the effectiveness of AI-assisted approaches for diabetes prevention and health equity.
研究类型
注册 (估计的)
阶段
- 不适用
联系人和位置
学习联系方式
- 姓名：Megha K Shah, M.D., M.Sc.
- 电话号码：404-778-6944
- 邮箱：mkshah@emory.edu
学习地点
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Georgia
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Atlanta、Georgia、美国、30322
- Emory University
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参与标准
资格标准
适合学习的年龄
- 成人
接受健康志愿者
描述
Inclusion Criteria:
Have at least one of the following diabetes risk factors:
- HbA1C of 5.7-6.4%
- BMI of 18.5 - 24.9 kg/m^2
- Fasting plasma glucose 100 - 125 mg/dL
- Family history of type 2 diabetes in first-degree relatives (parents, siblings, or children)
- History of gestational diabetes
- Membership in a high-risk ethnic group (African American, Hispanic, Native American, Asian American, Pacific Islander)
- Access to a smartphone or computer with internet
- English reading and writing proficiency
- Be willing to use the chatbot at least 3 times per week
- Be able to provide informed consent
- Availability for the 6-week study duration
- Able to perform resistance training exercises
Exclusion Criteria:
- Are diagnosed with type 1 or type 2 diabetics
- Have uncontrolled hypertension (higher than 160/100 mmHg)
- Have cardiovascular disease
- Are pregnant or planning to be pregnant
- Are actively undergoing treatment for cancer
- Have an eating disorder
- Have chronic kidney disease
- Are enrolled in another research study or participating in a structured lifestyle program
学习计划
研究是如何设计的？
设计细节
- 主要用途：预防
- 分配：不适用
- 介入模型：单组作业
- 屏蔽：无（打开标签）
武器和干预
参与者组/臂 |
干预/治疗 |
|---|---|
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实验性的：AI-Powered Diabetes Prevention Chatbot
Participants will receive access to an artificial intelligence (AI)-powered chatbot that provides personalized, culturally tailored diabetes prevention guidance focused on nutrition, resistance training, goal setting, and lifestyle behavior change for a 6-week intervention period.
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Participants will use a generative AI chatbot designed to provide personalized, evidence-based diabetes prevention support.
The chatbot delivers culturally tailored nutrition and physical activity recommendations, with an emphasis on high-protein dietary strategies, resistance training, goal setting, motivational support, and healthy lifestyle behaviors.
Participants will be encouraged to interact with the chatbot at least three times per week during the 6-week study period.
Recommendations are generated using expert-validated diabetes prevention content and personalized according to participant characteristics and preferences.
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研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Feasibility and Acceptability: Task completion rate
大体时间：Baseline, 6 weeks
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The proportion of recommended chatbot activities, goals, or behavioral tasks completed by participants during the 6-week intervention period, as assessed through chatbot analytics.
This measure will be used to evaluate participant engagement and adherence to chatbot-delivered recommendations.
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Baseline, 6 weeks
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Feasibility and Acceptability: Conversation length
大体时间：Baseline, 6 weeks
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Average number of messages exchanged between participants and the AI chatbot during each interaction session over the study period.
Conversation length will be obtained from chatbot analytics and used as an indicator of participant engagement with the intervention.
Longer conversations may reflect greater interaction with chatbot-delivered nutrition, physical activity, and lifestyle coaching content
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Baseline, 6 weeks
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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Feasibility: Body Mass Index (BMI)
大体时间：Baseline, 6 weeks
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Proportion of participants for whom self-reported body mass index (BMI, kg/m²) is successfully collected at baseline and at the end of the 6-week intervention period.
BMI will be calculated using participant-reported height and weight collected through study surveys.
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Baseline, 6 weeks
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Feasibility: Physical Activity Behaviors
大体时间：Baseline, 6 weeks
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Proportion of participants for whom self-reported physical activity behaviors, including engagement in resistance training and overall activity levels, will be successfully collected through study questionnaires and chatbot interaction data.
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Baseline, 6 weeks
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Feasibility: Dietary Behaviors
大体时间：Baseline, 6 weeks
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Proportion of participants for whom self-reported dietary intake and nutrition-related behaviors, including the adoption of higher-protein dietary practices and other healthy eating behaviors promoted by the chatbot intervention, are successfully collected.
Data will be collected through participant surveys and chatbot interaction records.
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Baseline, 6 weeks
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Feasibility: Behavioral Intention for Diabetes Prevention
大体时间：Baseline, 6 weeks
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Proportion of participants with successfully recorded self-reported intention and readiness to engage in healthy lifestyle behaviors for diabetes prevention, including dietary modifications and physical activity, as assessed through study questionnaires
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Baseline, 6 weeks
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Perceived Trustworthiness of the Chatbot
大体时间：Baseline, 6 weeks
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Participant-reported trust in the accuracy, reliability, and credibility of information and recommendations provided by the AI chatbot, assessed through study questionnaires.
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Baseline, 6 weeks
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Ease of Use: System Usability Scale (SUS)
大体时间：Baseline
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The SUS is a 10-item questionnaire that assesses participants' perceived usability of the application.
Each item is rated on a 5-point response scale.
Responses are scored using the standard SUS scoring method to generate a total score ranging from 0 to 100, with higher scores indicating greater perceived usability.
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Baseline
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NASA Task Load Index (NASA-TLX)
大体时间：Baseline
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The NASA-TLX assesses participants' perceived cognitive workload while using the application across six domains: mental demand, physical demand, temporal demand, perceived performance, effort, and frustration.
An overall workload score will be calculated from the domain ratings.
Scores range from 0 to 100, with higher scores indicating greater perceived workload or task demand.
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Baseline
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合作者和调查者
调查人员
- 首席研究员：Megha Shah, M.D., M.Sc.、Emory University
研究记录日期
研究主要日期
学习开始 (估计的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他研究编号
- 2025P013557
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)？
IPD 共享时间框架
IPD 共享支持信息类型
- 研究方案
- 树液
- 国际碳纤维联合会
- 分析代码
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