Menu
Not yet recruiting NCT07318233

Adaptive Self-Efficacy-Based AI Coaching for Cycling

No phase Interventional Exercise Training Exercise Behavior Exercise Adherence Challenges Motivation for Physical Activity

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Group 1: Self-efficacy-based AI coaching, Group 2: Static AI Affirmations.
Who it may be relevant to
Registry conditions: Exercise Training, Exercise Behavior, Exercise Adherence Challenges, Motivation for Physical Activity. Basic parameters: 18 years — 40 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
United States
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Official title

Adaptive Self-Efficacy-Based AI Coaching for Enhanced Indoor Cycling Performance: A Personalized Machine Learning Approach

Overview

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.

Interventions

  • Behavioral 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. 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 efficien
  • 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: * 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"

Primary outcome measures

  • Mean cycling power output during 20-minute time trial [Time frame: Day 2]

Eligibility criteria

Inclusion criteria

  • Age 18-40 years
  • Recreationally active
  • Familiar with stationary cycling
  • Able to complete 20 minutes of vigorous cycling

Exclusion criteria

  • Cardiovascular, metabolic, or respiratory conditions
  • Medications affecting heart rate response
  • Lower extremity injury within past 3 months
  • Competitive cyclists (>10 hours cycling/week)
  • Pregnancy

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Single blind
Primary purpose
Basic science

Study locations

United States · 1 center
  • University of Miami — Coral Gables

Identifiers

NCT: NCT07318233 · 20251354

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗