Recruiting

WorkoutCPP

Sponsor:

Icahn School of Medicine at Mount Sinai

Code:

NCT07810218

Conditions

Pelvic Pain

Endometriosis

Chronic Pelvic Pain

Eligibility Criteria

Sex: Female

Age: 18 - 55

Healthy Volunteers: Not accepted

Interventions

Reinforcement Learning (RL)-Based Personalized Exercise Recommendations

Generic Exercise Recommendation

Study Details

Brief 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.

Conditions

Pelvic Pain

Endometriosis

Chronic Pelvic Pain

Study ID

NCT07810218

Start date

Feb 6, 2026

Status verified date

Sep, 2026

Completion date

Aug, 2027

Anticipated

Primary completion date

Aug, 2027

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: Female

Age: 18 - 55

Healthy Volunteers: Not accepted

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).

Study Design

Enrollment

45 participants

Anticipated

Allocation

Randomized

Intervention Model

Crossover

Primary purpose

Other

Interventions and Outcome Measures

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.

Interventions

Reinforcement 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.

Generic 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.

Primary outcome measure

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

Central Contacts and Locations

Central contacts

Ipek Ensari, PhD

631-565-1829ipek.ensari@mssm.edu

Gerard M Ona, MD

347-835-8115GerardAnneAprilOna@mssm.edu

Locations

Icahn School of Medicine at Mount Sinai

Recruiting

New York, New York, United States, 10029

Contacts

Ipek Ensari, PhD

631-565-1829ipek.ensari@mssm.edu

Gerard M Ona, MD

347-835-8115GerardAnneAprilOna@mssm.edu

Principal Investigator:

Ipek Ensari

More Information

Sponsor

Icahn School of Medicine at Mount Sinai

Last update posted

Sep 9, 2026

Last verified

Sep, 2026

Keywords

  • Mobile health
  • Reinforcement learning
  • Physical activity
  • N-of-1 trial
  • exercise
  • personalized
  • intervention

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by Icahn School of Medicine at Mount Sinai on 2026-09-09.