Exercise TrainingAINCT07318233
Adaptive Self-Efficacy-Based AI Coaching for Cycling
A study testing Group 1: Self-efficacy-based AI coaching and Group 2: Static AI Affirmations in people with Exercise Training and Exercise Behavior, run by University of Miami at 1 site in United States.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 40 yearsHealthy volunteers can take part
- Conditions the study is for
- Exercise Training, Exercise Behavior, Exercise Adherence Challenges
- What is being tested?
- Group 1: Self-efficacy-based AI coaching and Group 2: Static AI Affirmations
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 120 participants
- Where
- 1 siteUnited States
- Study dates
- 15 May 2026to 28 Dec 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 40 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- Age 18-40 years
- Recreationally active
- Familiar with stationary cycling
- Able to complete 20 minutes of vigorous cycling
Who cannot join
- Cardiovascular, metabolic, or respiratory conditions
- Medications affecting heart rate response
- Lower extremity injury within past 3 months
- Competitive cyclists (>10 hours cycling/week)
- Pregnancy
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
University of Miami
Coral Gables, Florida, 33146
Anna Queiroz, Ph.D., 305-284-3752, aqueiroz@miami.edu
Meshak Cole, B.S., 305 2843752, mwc94@miami.edu
About this study
Official title: Adaptive Self-Efficacy-Based AI Coaching for Enhanced Indoor Cycling Performance: A Personalized Machine Learning Approach
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.
- Conditions
- Exercise Training, Exercise Behavior, Exercise Adherence Challenges, Motivation for Physical Activity, Motivational Enhancement
- Being tested
- Group 1: Self-efficacy-based AI coaching, Group 2: Static AI Affirmations
- Keywords
- Physical Activity, AI affirmations, just-in-time adaptive intervention, cycling
- Medical subject headings
- Motor Activity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Group 1: Self-efficacy-based AI coachingBehavioural
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 AffirmationsBehavioural
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 group | Role | What they receive |
|---|---|---|
| Control Group | No intervention | 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 | Experimental | The Thompson Sampling contextual bandit algorithm, trained on Session 1 data, monitors performance continuously and evaluates every 5 seconds whether to deliver an affirmation. Receives: Behavioral: Group 1: Self-efficacy-based AI coaching |
| Group 2: Static AI Affirmations | Active comparator | 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. Receives: Behavioral: Group 2: Static AI Affirmations |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Day 2
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jan 2026
- Study start15 May 2026
- Primary completion23 Dec 2028 (estimated)
- Study completion28 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Exercise TrainingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Miami
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Anna Queiroz, University of Miami
- Investigators
- Anna Queiroz, Ph.D., Principal investigator, University of Miami
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07318233
- Sponsor's protocol ID
- 20251354
- Acronym
- AI
- Status last verified
- Aug 2026
- First submitted
- 19 Dec 2025
- Last updated
- 17 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Adaptive Self-Efficacy-Based AI Coaching for Enhanced Indoor Cycling Performance: A Personalized Machine Learning Approach
- ClinicalTrials.gov ID
- NCT07318233
- Acronym / sponsor ID
- AI / 20251354
- Conditions
- Exercise Training, Exercise Behavior, Exercise Adherence Challenges, Motivation for Physical Activity, Motivational Enhancement
- Keywords
- Physical Activity, AI affirmations, just-in-time adaptive intervention, cycling
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- University of MiamiUniversity, hospital or other
- Study start
- 15 May 2026 (actual)
- Primary completion
- 23 Dec 2028 (estimated)
- Study completion
- 28 Dec 2028 (estimated)
- First posted
- 5 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Control Group | No intervention | –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 | Experimental | Behavioral: Group 1: Self-efficacy-based AI coachingThe Thompson Sampling contextual bandit algorithm, trained on Session 1 data, monitors performance continuously and evaluates every 5 seconds whether to deliver an affirmation. |
| Group 2: Static AI Affirmations | Active comparator | Behavioral: Group 2: Static AI AffirmationsGeneric 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. |
Who may qualify
- Ages
- 18 to 40 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (4 listed)
- Age 18-40 years
Key exclusion criteria (5 listed)
- Cardiovascular, metabolic, or respiratory conditions
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Group 1: Self-efficacy-based AI coachingThe 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). | Behavioural | Group 1: Self-efficacy-based AI coaching |
| Group 2: Static AI AffirmationsGeneric 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" | Behavioural | Group 2: Static AI Affirmations |
Primary outcome
Mean cycling power output during 20-minute time trial
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Aug 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Anna Queiroz, Ph.D. principal investigator
University of Miami
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Group 1: Self-efficacy-based AI coaching and Group 2: Static AI Affirmations in people with Exercise Training and Exercise Behavior, run by University of Miami at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 17 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Anna Queiroz, Ph.D. (305-284-3752, aqueiroz@miami.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mean cycling power output during 20-minute time trial (time frame: Day 2). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Aug 2026.