RecruitingNot ApplicableNCT07810218

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

Sponsor

Icahn School of Medicine at Mount Sinai

Enrollment

45 participants

Start Date

Feb 6, 2026

Study Type

INTERVENTIONAL

Conditions

Endometriosis Pelvic Pain Chronic pelvic pain

Summary

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.

Eligibility

Sex: FEMALEMin Age: 18 YearsMax Age: 55 Years

Plain Language Summary

Simplified for easier understanding

This clinical trial is studying a behavioral approach called Generic Exercise Recommendation and a behavioral approach called Reinforcement Learning (RL)-Based Personalized Exercise Recommendations for people with chronic pelvic pain, endometriosis, and other related conditions. The study is currently recruiting participants at 1 location. People eligible for this study include women aged 18 Years to 55 Years.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

Interested in this trial?

Get notified about updates and connect with the research team.

Interventions

BEHAVIORALReinforcement Learning (RL)-Based Personalized Exercise Recommendations

Daily exercise recommendations (using type, intensity, and duration) are generated by a contextual bandit reinforcement learning agent, based on the implementation described in Meier et al. 2023. Recommendations are personalized using each participant's initially generated list of exercises based on their physical ability and resources available, as well as contextual daily factors including pain symptoms, prior exercise compliance, and their feedback to the previous exercise recommendation.

BEHAVIORALGeneric Exercise Recommendation

Participants receive exercise recommendations from a standardized, set list of exercise recommendations that are based on USDHHS physical activity guidelines (Piercy et al., 2020). Recommendations are not personalized based on participant contextual information and do not adapt over the course of the study.