AI Coaching for Exercise Motivation
(AI Trial)
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial aims to determine if AI-based coaching can boost cycling power during a 20-minute ride. Participants will receive either personalized AI coaching (self-efficacy-based AI coaching), generic motivational messages, or no affirmations. This trial suits those who are recreationally active, familiar with stationary bikes, and can handle 20 minutes of intense cycling. Individuals without heart, lung, or metabolic issues and not on medications affecting heart rate may qualify. As an unphased trial, this study offers a unique opportunity to explore innovative AI coaching methods and contribute to cutting-edge research.
Will I have to stop taking my current medications?
If you are taking medications that affect heart rate response, you may need to stop them to participate in this trial.
What prior data suggests that self-efficacy-based AI coaching is safe for exercise motivation?
Research shows that AI coaching for exercise is safe and easy to handle. Studies have found that AI coaching helps people stay motivated and adhere to their exercise routines. With AI guidance, individuals are more likely to follow their workout plans. No direct evidence indicates any harmful effects from AI coaching in humans. Instead, participants often feel more confident and encouraged to stay active. Overall, AI coaching appears to be a safe method for enhancing exercise motivation.12345
Why are researchers excited about this trial?
Unlike traditional exercise motivation techniques, which often rely on static encouragement or self-motivation, the self-efficacy-based AI coaching treatment uses a smart algorithm to tailor affirmations based on individual performance. This AI-driven approach continuously monitors a participant's exercise session and adapts in real-time, offering personalized motivation just when it's needed. Researchers are excited because this dynamic method could significantly boost exercise adherence and effectiveness by catering to personal needs rather than applying a one-size-fits-all solution.
What evidence suggests that self-efficacy-based AI coaching is effective for enhancing cycling performance?
Research has shown that AI coaching, which focuses on building confidence, can effectively increase motivation and performance. In this trial, participants in the "Group 1: Self-efficacy-based AI coaching" arm will receive personalized feedback using a Thompson Sampling contextual bandit algorithm to enhance motivation. Studies have found that AI coaching positively affects mental well-being by boosting motivation and confidence. For example, AI tools can improve critical thinking and motivation by increasing self-belief. One study found that AI significantly increased motivation for physical activity in teenagers. These findings suggest that AI coaching can help improve exercise results, like cycling power, by providing personalized feedback and support.13678
Who Is on the Research Team?
Anna Queiroz, Ph.D.
Principal Investigator
University of Miami
Are You a Good Fit for This Trial?
This trial is for recreationally active people aged 18-40 who are familiar with stationary cycling and can handle 20 minutes of vigorous exercise. It's not specified who cannot join, but typically those with health issues preventing intense exercise would be excluded.Inclusion Criteria
Exclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Treatment
Participants engage in a 20-minute indoor cycling time trial with different AI coaching interventions
Follow-up
Participants are monitored for safety and effectiveness after treatment
What Are the Treatments Tested in This Trial?
Interventions
- Self-efficacy-based AI coaching
Trial Overview
The study tests if personalized AI coaching that adapts to your real-time data can improve your power during a 20-minute indoor cycling time trial, compared to just getting static affirmations or no AI guidance at all.
How Is the Trial Designed?
3
Treatment groups
Experimental Treatment
Active Control
The Thompson Sampling contextual bandit algorithm, trained on Session 1 data, monitors performance continuously and evaluates every 5 seconds whether to deliver an affirmation.
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.
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.
Find a Clinic Near You
Who Is Running the Clinical Trial?
University of Miami
Lead Sponsor
Citations
Systematic review exploring human, AI, and hybrid health ...
AI coaching generally reported positive improvements in psychological wellbeing, including reductions in depressive symptoms (49, 60, 67) and ...
The role of AI tools on EFL students' motivation, self ...
The results revealed that AI indirectly boosts critical thinking awareness by enhancing self-efficacy and motivation.
An Artificial Intelligence Exercise Coaching Mobile App
This study aimed to use deep neural networks to develop a personal workout assistant that offers feedback on squat postures using only mobile devices.
Enhancing adolescents' exercise motivation management ...
Results: This study found that AI anthropomorphism is significantly associated with adolescents' motivation for physical activity. Further analysis reveals that ...
A critical evaluation of OpenAI's GPT-4 model - PubMed Central
Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 model
Evaluation Strategies for Large Language Model-Based ...
Objective: This scoping review systematically maps current evaluation strategies for LLM-based AI coaches in exercise and health, identifies ...
Impact of Digital Health Coaching on Behavioral Change
Digital platforms can bolster self-efficacy through goal setting, progress tracking, and positive feedback, while virtual communities and peer ...
Development of a Coaching Protocol to Enhance Self ...
To describe the development of the Specific, Measurable, Action-Oriented, Realistic, and Timed (SMART) Coaching Protocol to increase exercise self-efficacy.
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