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Personalized Exercise Recommendations for Chronic Pelvic Pain Using Reinforcement Learning

WorkoutCPP: A Pilot Series of N-of-1 Trials Evaluating RL-Generated Adaptive Exercise Recommendations for Pelvic Pain Management

Who can join

Ages 18 to 55 · Women

Inclusion criteria:

* 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 criteria:

* 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.
* Major life events expected during the next 10 weeks (e.g., pregnancy, planned surgery, or extended travel likely to interfere with participation).
* 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).

About the study

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.

What is being tested

• Reinforcement Learning (RL)-Based Personalized Exercise Recommendations (behavioral)
• Generic Exercise Recommendation (behavioral)

Sponsor: Icahn School of Medicine at Mount Sinai · Participants: 45 · Started: Feb 6, 2026

Contact the study team

• Ipek Ensari, PhD · Phone: 631-565-1829
• Gerard M Ona, MD · Phone: 347-835-8115

Official record on ClinicalTrials.gov — NCT07810218

Locations in the U.S.

Conditions

• Pelvic Pain
• Endometriosis

From ClinicalTrials.gov, data retrieved Sep 30, 2026. Each study sets its own eligibility; the study team decides who can join.