An interventional study of Reinforcement Learning (RL)-Based Personalized Exercise Recommendations and Generic Exercise Recommendation in Pelvic Pain, Endometriosis and Chronic Pelvic Pain, sponsored by Icahn School of Medicine at Mount Sinai. Recruiting at 1 site in United States. Open to female participants aged 18 Years to 55 Years. Per ClinicalTrials.gov, last updated 2026-09-09.
Sponsored by Icahn School of Medicine at Mount Sinai · Not applicable, Interventional, and Other
Among 151 studies in Pelvic Pain with a site in United States — 36 open to participants now, 39 under way, 1 of them phase 3. Pelvic Pain · United States.
Among the 151 studies in Pelvic Pain with a site in United States, this one is the 65th largest by planned enrollment of the 123 interventional ones, is one of 14 that started in 2026, is one of 71 with no phase, reaches primary completion later than 18 of the 38 other live ones, and was the 151st registered.
375 studies on the registry are indexed under Pelvic Pain; 89 are open to participants now.
This study's planned enrollment of 45 is below the median of 60 across 277 interventional studies indexed under Pelvic Pain.
Browse Pelvic Pain studies →36 of the 151 studies in Pelvic Pain with a site in United States are open now, in 1 country. Each count lists the studies.
Of the 59 Pelvic Pain studies recruiting now, 21 (36%) set no upper age limit and 36 (61%) stop at 75 or younger; 5 take children under 18; 10 take healthy volunteers.
This study: 18 Years to 55 Years · female · no healthy volunteers.
The full table on the hub →151 studies; the count open now beside each. This study's row is marked.
First in 1998; this study’s year is marked. 14 started in 2026.
Median enrollment 50 across the 123 interventional studies, 104 across the 28 observational. Of the 96 that completed or were terminated, 35 (36%) have results posted.
18 of 151 have an industry lead sponsor. The 10 most frequent:
Icahn School of Medicine at Mount Sinai is the lead sponsor of 776 studies on the registry; 176 are open to participants now.
Of its 125 completed or terminated interventional studies of FDA-regulated products, 83 (66%) have results posted.
Icahn School of Medicine at Mount Sinai has 1 study filed under Pelvic Pain — fewer than ten, so there is no comparison to draw.
15 of the studies in Pelvic Pain with a site in United States that are under way reach primary completion within a year, 0 of them phase 3. Phase 3 first, then by date:
| Study | Primary completion |
|---|---|
| NCT06110065University of ArizonaCT-guided Pudendal Nerve Cryoneurolysis for Palliation of Malignancy-induced Pelvic Pain | 2026-11 |
| NCT05354869University of California, Los AngelesTransvaginal Electrical Stimulation for Myofascial Pelvic Pain | 2026-12 |
| NCT03794466University of Kansas Medical CenterQuantify the Degree of Pain Relief of Pelvic Congestion Syndrome Following Gonadal Vein Embolization | 2026-12-31 |
| NCT06083597Weill Medical College of Cornell UniversityDevelopment of a Patient-reported Outcome Measure for Women With Chronic Pelvic Pain | 2026-12-31 |
| NCT06611501Boston Children's HospitalGlycemic Regulation as Endometriosis Adjunct Treatment | 2027-01 |
Among the 151 studies in Pelvic Pain with a site in United States, from their registry updates:
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.
Chronic pelvic pain disorders (CPPDs) are associated with high symptom burden and reduced quality of life. Physical activity (PA) and exercise have emerged as a promising non-pharmacological approach for symptom management. However, optimal exercise type, intensity, and timing for pain management vary substantially across individuals, supporting the need for personalized adaptive approaches (Ensari et al., 2022, Krasny-Pacini et al., 2017). This study will enroll participants will a CPPD diagnosis into a remote, 9-week study to evaluate the feasibility of RL-based personalized exercise to non-personalized, standard recommendations. Enrollment is rolling, with participants entering the study on a continuous basis. Each participant's start date, and their 9-week intervention period, is determined by their baseline interview date. A baseline interview upon enrollment is scheduled with an exercise physiologist to review the participant's initial exercise list and provide exercise safety information, as well as overview use of the study App. Participants can choose to stay in the study for 2 additional weeks to make up any weeks with inadequate adherence. Study outcomes are measured daily over the course of the intervention period. Daily App-based tracking items assess pain and other symptoms, exercise behavior, perceived effect and feedback to the recommendation, menstrual status, and recommendation compliance. Fitbit trackers simultaneously track participants' objectively-estimated PA. A reinforcement learning (RL) agent implemented in Meier et al. 2023 as the middleware platform generates daily personalized exercise recommendations delivered via a research mobile phone application (Hirten et al., 2023, Meier et al., 2023). Participant-reported perceived effect of each exercise recommendation is used by the RL agent to calculate reward. Participants serve as their own controls, allowing for within-person comparison under the two conditions (Krasny-Pacini et al., 2017). Primary outcomes for the study include standard study feasibility metrics (e.g., adherence, retention). Secondary outcomes focus on RL agent performance and learning over time. Participant safety will be monitored throughout the study, in accordance with the institutional review board.
This work was supported by the Digital Health Partnership (DHP), a collaboration between the Hasso Plattner Institute, Data4Life, the Windreich Department of Artificial Intelligence and Human Health, the Hasso Plattner Institute for Digital Health at Mount Sinai, and The Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
A reason a treatment, test or procedure should not be given to a particular person, such as an allergy, another illness or a medicine they take.
Contraindication
A reason a treatment, test or procedure should not be given to a particular person, such as an allergy, another illness or a medicine they take.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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.
Behavioral: Reinforcement Learning (RL)-Based Personalized Exercise Recommendations
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.
Behavioral: Generic Exercise Recommendation
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.
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.
Exercise Recommendation Adherence Rate
Exercise recommendation adherence rate is calculated by the proportion of daily exercise recommendations completed over the course of the intervention. A higher exercise adherence rate indicates that participants are completing their given exercise recommendations at higher frequencies.
Time frame: At 9 weeks at study completion
Participant Retention Rate
Participant retention rate is the proportion of enrolled participants completing study participation until the end of intervention. A higher retention rate indicates that participants complete the 9-week intervention period at higher frequencies.
Time frame: At 9 weeks at study completion
Reinforcement Learning Agent Action Entropy Over Time
Entropy of the reinforcement learning (RL) agent's action probability distributions is calculated at each decision point throughout the study period. Entropy ranges from a minimum of 0 (the agent selects a single action with certainty) to a maximum of log(K), where K is the number of distinct recommendation actions available to the agent at that decision point (maximum entropy occurs when the agent assigns equal probability to all available actions). Decreasing entropy over time indicates increasing model confidence and more consistent personalized recommendation patterns. Higher entropy indicates greater uncertainty in the agent's decision making. This outcome will be evaluated for the entire duration of the intervention based on the daily data.
Time frame: At 9 weeks at study completion
Plan to share: No — Individual participant data will not be shared, in order to protect participant privacy and confidentiality, consistent with the IRB guidelines and informed consent form under which participants were enrolled.
Started Feb 2026 (actual). Primary completion is estimated for Aug 2027, 18 months after the start.
Of 110 non-industry interventional studies with no phase under Pelvic Pain, half reached primary completion within 17 months of starting (the median), counting those still running; a quarter within 5 and three quarters within 30. This study's planned 18 months is close to that.
Counting every sponsor, half of 114 studies reached it within 17 months; a quarter within 5 and three quarters within 30.
How these figures are worked out, and which studies are left out of them.
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