EligibleTrials

Personalized Exercise Recommendations for Chronic Pelvic Pain Using Reinforcement Learning

Recruiting · NCT07810218 · Interventional (participants receive a specific treatment)

View the official record on ClinicalTrials.gov →
Endometriosis

Interventions studied

Generic Exercise RecommendationReinforcement Learning (RL)-Based Personalized Exercise Recommendations

What this trial is about

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.

Who can take part

Age range
18 Years to 55 Years
Sex
Female
Healthy volunteers
No - a diagnosis or condition is required
Phase
Not applicable (e.g. observational or device study)
Study type
Interventional (participants receive a specific treatment)

Inclusion criteria

Exclusion criteria

Where it is running

1 location listed across 1 US state.

Read the full protocol, contacts and eligibility on ClinicalTrials.gov →

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