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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
BASIC_SCIENCE
Masking
SINGLE
Enrollment
120
The Thompson Sampling contextual bandit algorithm, trained on Session 1 data, monitors performance continuously and evaluates every 5 seconds whether to deliver an affirmation. The policy is trained to maximize a multi-objective "efficacy-preserving performance" function that rewards: * Maintaining target power relative to rolling 30s/2min/5min baselines * Stabilizing short-horizon power variability (30s coefficient of variation) * Stabilizing heart-rate (HR) trajectory consistent with efficient pacing The decision process considers: * Current power relative to 30-second, 2-minute, and 5-minute rolling averages * Power output variability (coefficient of variation over past 30 seconds) * Heart rate trajectory and cardiac drift patterns * Cadence stability and changes from baseline * Time elapsed and expected fatigue progression based on power-duration curve Self-efficacy-based AI coaching adapts to physiological measures (power and heart rate).
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: * Minutes 3, 6: "You're building momentum with every pedal stroke-maintain this strong rhythm" * Minutes 9, 12: "Strong effort-push through this challenge" * Minutes 15, 18: "Final push-finish strong"
University of Miami
Coral Gables, Florida, United States
Mean cycling power output during 20-minute time trial
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.
Time frame: Day 2
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