Adaptive Self-Efficacy-Based AI Coaching for Cycling

Part of paid clinical trials in Coral Gables, Florida.

Sponsor
University of Miami
Study ID
NCT07318233
Status
Not Yet Recruiting

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Conditions

  • Exercise Adherence Challenges
  • Exercise Behavior
  • Exercise Training
  • Motivation for Physical Activity
  • Motivational Enhancement

Eligibility Criteria

Sex
ALL
Age
18 Years - 40 Years
Healthy Volunteers
Accepted

Interventions

  • Group 1: Self-efficacy-based AI coaching — BEHAVIORAL
    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).
  • Group 2: Static AI Affirmations — BEHAVIORAL
    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"

Study Details

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.

Key Dates

Start date
Jun 1, 2026
Status verified
Feb 2026
Primary completion
Dec 23, 2028
Completion
Dec 28, 2028

Study Design

Enrollment
120 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
BASIC_SCIENCE

Arms

  • No Intervention: 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.
  • Experimental: 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.
  • Active Comparator: 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.

Primary Outcome Measure

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

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
University of MiamiCoral GablesFlorida33146
Anna Queiroz, Ph.D.
[email protected]
305-284-3752
Meshak Cole, B.S.
[email protected]
305 2843752

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University of Miami· Coral Gables, FL

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