Testing AI-adjusted daily exercise advice for chronic pelvic pain
Tests AI-adjusted exercise advice in adults with chronic pelvic pain.
Plain-language title generated from the registry record
Registry title: Personalized Exercise Recommendations for Chronic Pelvic Pain Using Reinforcement Learning
Key facts
- Where
- New York, United StatesSee the site
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 18 and 55 years old?
Source: NCT07810218, who can take part
Where in United States
- Icahn School of Medicine at Mount SinaiNew York, United StatesTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Icahn School of Medicine at Mount Sinai
- Trial code
- NCT07810218
- Record updated
- 9 September 2026
- Original description
WorkoutCPP is a pilot study evaluating the feasibility of a personalized exercise recommendation system for individuals with chronic pelvic pain disorders (CPPDs). The study uses reinforcement learning (RL), a type of artificial intelligence that adapts recommendations over time based on each participant's reported pain levels, symptom burden, and exercise compliance. Participants receive daily exercise recommendations that alternate between standard, non-personalized guidance and personalized, RL-generated recommendations across four 2-week phases, allowing within-person comparison of outcomes under each condition. The primary hypothesis is that an RL-based adaptive recommendation system is feasible to deliver in a CPPD population.Read full original descriptionShow less
WorkoutCPP: A Pilot Series of N-of-1 Trials Evaluating RL-Generated Adaptive Exercise Recommendations for Pelvic Pain Management
WorkoutCPP is a pilot study evaluating the feasibility of a personalized exercise recommendation system for individuals with chronic pelvic pain disorders (CPPDs). The study uses reinforcement learning (RL), a type of artificial intelligence that adapts recommendations over time based on each participant's reported pain levels, symptom burden, and exercise compliance. Participants receive daily exercise recommendations that alternate between standard, non-personalized guidance and personalized, RL-generated recommendations across four 2-week phases, allowing within-person comparison of outcomes under each condition. The primary hypothesis is that an RL-based adaptive recommendation system is feasible to deliver in a CPPD population.
Inclusion: Self-reported CPPD (e.g., endometriosis, adenomyosis, fibroids, etc.) based on clinician diagnosis; Aged 18-55 years; Ownership of an iOS or Android smartphone; Willingness to self-track daily symptoms, exercise activities, and self-management behaviors using a smartphone research app; Willingness to wear an activity tracker for the study duration; Willingness to follow exercise recommendations from a smartphone research app, provided no adverse symptoms occur; Ability to read and write in English sufficient to understand study materials and communications; At least intermittently physically active (e.g., ≥30 minutes of walking twice per week).
Exclusion: Major life events expected during the next 10 weeks (e.g., pregnancy, planned surgery, or extended travel likely to interfere with participation); Absolute contraindications to PA (e.g., recent myocardial infarction, complete heart block, acute congestive heart failure, unstable angina, or uncontrolled severe hypertension, BP ≥180/110 mm Hg); More than two "Yes" responses on the Physical Activity Readiness Questionnaire (PAR-Q) (16) without physician clearance; Current or planned pregnancy within the next 6 months; Having given birth in the past 6 months or currently nursing; Inability to wear an activity tracker or use the app for the study duration; Complete inactivity (i.e., <60 minutes of moderate-intensity PA per week).