Personalized Exercise Recommendations for Chronic Pelvic Pain Using Reinforcement Learning

Conditions

• Chronic Pelvic Pain
• Endometriosis
• Pelvic Pain

Eligibility Criteria

Sex

FEMALE

Age

18 Years - 55 Years

Healthy Volunteers

Not accepted

Interventions

• Reinforcement Learning (RL)-Based Personalized Exercise Recommendations — BEHAVIORAL
  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.
• Generic Exercise Recommendation — BEHAVIORAL
  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.

Study Details

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.

Key Dates

First listed

Sep 9, 2026

Start date

Feb 6, 2026

Status verified

Sep 2026

Primary completion

Aug 31, 2027

Completion

Aug 31, 2027

Study Design

Enrollment

45 participants (estimated)

Allocation

RANDOMIZED

Intervention model

CROSSOVER

Primary purpose

OTHER

Arms

• Experimental: RL-based personalized phase
  Participants will receive RL-generated personalized exercise recommendations, which are generated using the list from the initial participant intake form indicating their capacity and resources for carrying out various modalities and intensities of physical activity. The RL agent learns from the participant feedback to update the update the subsequent recommendations.
• Active Comparator: Standard (Generic) Exercise Arm
  Participants will receive standardized, non-personalized exercise recommendations based on the U.S. Physical Activity Guidelines, in 2-week blocks. This comparison will serve as the "active control" arm to which the experimental RL arm will be compared. This type of control condition was selected to provide a more rigorous test of the experimental condition.

Primary Outcome Measure

Exercise Recommendation Adherence Rate [ Time Frame: At 9 weeks at study completion ]

Central Contacts

• Ipek Ensari, PhD
  631-565-1829
  [email protected]
• Gerard M Ona, MD
  347-835-8115
  [email protected]

Locations (1)

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