Adaptive Self-Efficacy-Based AI Coaching for Enhanced Indoor Cycling Performance: A Personalized Machine Learning Approach
概览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 120
- 试验地点
- 1
- 主要终点
- Mean cycling power output during 20-minute time trial
概览
简要总结
The primary objective of this study is to evaluate whether adaptive, AI-delivered personalized self-efficacy-based AI coaching based on real-time physiological and performance feedback enhance indoor cycling power output during a 20-minute time trial compared to static affirmations and exercise-only control conditions.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Basic Science
- 盲法
- Single (Participant)
入排标准
- 年龄范围
- 18 Years 至 40 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Age 18-40 years
- •Recreationally active
- •Familiar with stationary cycling
- •Able to complete 20 minutes of vigorous cycling
排除标准
- •Cardiovascular, metabolic, or respiratory conditions
- •Medications affecting heart rate response
- •Lower extremity injury within past 3 months
- •Competitive cyclists (\>10 hours cycling/week)
- •Pregnancy
研究组 & 干预措施
Control Group
No affirmations delivered. Participants receive only time notifications at 5, 10, 15, and 19 minutes for pacing awareness. Same equipment worn to control for potential monitoring effects.
Group 1: Self-efficacy-based AI coaching
The Thompson Sampling contextual bandit algorithm, trained on Session 1 data, monitors performance continuously and evaluates every 5 seconds whether to deliver an affirmation.
干预措施: Group 1: Self-efficacy-based AI coaching (Behavioral)
Group 2: Static AI Affirmations
Generic motivational messages delivered at fixed intervals (minutes 3, 6, 9, 12, 15, and 18) regardless of performance state. Messages follow the same complexity gradient based on elapsed time rather than individual response.
干预措施: Group 2: Static AI Affirmations (Behavioral)
结局指标
主要结局
Mean cycling power output during 20-minute time trial
时间窗: Day 2
Average cycling power output over the full 20-minute time trial. The outcome compares mean power between intervention arms (adaptive AI coaching vs. static affirmations vs. exercise-only control). Power is captured continuously via the cycling ergometer and summarized as the mean watts for each participant's trial.
次要结局
未报告次要终点
研究者
Anna Queiroz
Associate Professor
University of Miami