Enhancing Physical Activity With LLM-Generated Coaching Prompts: A My Heart Counts Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 50
- 主要终点
- Physical Activity Levels
研究概览
简要总结
This pilot study aims to evaluate whether personalized coaching prompts generated by a large language model (LLM) can effectively increase physical activity levels among participants. The study will involve 50 participants who will receive daily text messages, either personalized by the LLM or generic, over a 14-day period. Participants will share their HealthKit data for analysis. The findings will inform the development of future versions of the My Heart Counts application, enhancing user engagement and health outcomes.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Crossover
- 主要目的
- Basic Science
- 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
- •Age: Participants must be 18 years or older.
- •Health Status: Participants must be healthy enough to engage in physical activity.
- •Language Proficiency: Participants must be able to read, understand, and consent in English or Spanish (dependent on which is being studied).
- •Device Ownership: Participants must possess an iPhone and an Apple Watch.
- •Data Sharing: Participants must be willing to share their HealthKit data via secure upload.
排除标准
- •Age: Participants under the age of
- •Health Status: Participants who are not healthy enough to engage in physical activity.
- •Language Proficiency: Participants who cannot read, understand, or consent in English or Spanish.
- •Device Ownership: Participants who do not have both an iPhone and an Apple Watch.
- •Data Sharing: Participants unwilling to share their HealthKit data via secure upload.
研究组 & 干预措施
Personalized LLM-Generated Coaching Prompts
干预措施: Pre-Generated Personalized LLM Coaching Impact (Behavioral)
Generic Activity Prompts
干预措施: Pre-Generated Personalized LLM Coaching Impact (Behavioral)
结局指标
主要结局
Physical Activity Levels
时间窗: 14 days from enrollment
次要结局
未报告次要终点
研究者
Euan Ashley
Fellow, Cardiovascular Medicine
Stanford University