Personalized Exercise Recommendations for Chronic Pelvic Pain Using Reinforcement Learning

NCT07810218 | Recruiting

• 1 other identifier: STUDY-23-00721
• Study Type: interventional
• Target: 45
• Locations: 1 country
• Sites: 1

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

Keywords

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

Outcome Measures

Primary Outcomes (2)

• 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.

  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.

  At 9 weeks at study completion

Secondary Outcomes (1)

• Reinforcement Learning Agent Action Entropy Over Time

  At 9 weeks at study completion

Study Arms (2)

RL-based personalized phase

EXPERIMENTAL

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

Standard (Generic) Exercise Arm

ACTIVE COMPARATOR

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

Interventions

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.

Standard (Generic) Exercise Arm

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.

RL-based personalized phase

Eligibility Criteria

• Age: 18 Years - 55 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64)

You may qualify if:

• 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).

You may not qualify if:

• 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).

Sponsors & Collaborators

• Icahn School of Medicine at Mount Sinai: lead
• Hasso Plattner Institute, Potsdam, Germany: collaborator

Study Sites (1)

Icahn School of Medicine at Mount Sinai

New York, New York, 10029, United States

RECRUITING

Related Publications (5)

• Ensari I, Lipsky-Gorman S, Horan EN, Bakken S, Elhadad N. Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation. BMJ Open. 2022 Jul 18;12(7):e059280. doi: 10.1136/bmjopen-2021-059280.

  PMID: 35851021 BACKGROUND

• Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, George SM, Olson RD. The Physical Activity Guidelines for Americans. JAMA. 2018 Nov 20;320(19):2020-2028. doi: 10.1001/jama.2018.14854.

  PMID: 30418471 BACKGROUND

• Hirten RP, Danieletto M, Landell K, Zweig M, Golden E, Orlov G, Rodrigues J, Alleva E, Ensari I, Bottinger E, Nadkarni GN, Fuchs TJ, Fayad ZA. Development of the ehive Digital Health App: Protocol for a Centralized Research Platform. JMIR Res Protoc. 2023 Nov 16;12:e49204. doi: 10.2196/49204.

  PMID: 37971801 BACKGROUND

• Krasny-Pacini A, Evans J. Single-case experimental designs to assess intervention effectiveness in rehabilitation: A practical guide. Ann Phys Rehabil Med. 2018 May;61(3):164-179. doi: 10.1016/j.rehab.2017.12.002. Epub 2017 Dec 15.

  PMID: 29253607 BACKGROUND

• Rabbi M, Aung MS, Gay G, Reid MC, Choudhury T. Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults. J Med Internet Res. 2018 Oct 26;20(10):e10147. doi: 10.2196/10147.

  PMID: 30368433 BACKGROUND

Related Links

• Study protocol registry on the Open Science Framework (OSF)

MeSH Terms

Conditions

Pelvic Pain; Endometriosis; Motor Activity

Condition Hierarchy (Ancestors)

Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases; Behavior

Study Officials

• Ipek Ensari, PhD

  Icahn School of Medicine at Mount Sinai

  PRINCIPAL INVESTIGATOR

• Stefan Konigorski, PhD

  Department of Computational Precision Nutrition, German Institute of Human Nutrition

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Ipek Ensari, PhD

CONTACT

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

Gerard M Ona, MD

CONTACT

347-835-8115; GerardAnneAprilOna@mssm.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Masking Details: Participants are blinded to their assigned study phase (active control: generic exercise recommendations vs. experimental: RL-generated adaptive recommendations) and are not informed of the phase sequence or their current assignment at any point during the study. However, participants may be able to infer their assigned phase over time based on the nature of the recommendations received. Study investigators and the data analysis team are not blinded to phase assignment.
• Purpose: OTHER
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Model Details: N-of-1 randomized crossover design in which each participant serves as their own control, randomized 1:1 to one of two intervention sequences (ABAB or BABA), alternating between RL-generated personalized exercise recommendations (active arm) and standard generic recommendations (control arm) across multiple 2-week blocks within the 9-week study period. First week is treated as a baseline week where participants get accommodated to the study App and procedures, as well as RL agent warm-up.

Study Record Dates

• First Submitted: September 3, 2026
• First Posted: September 9, 2026
• Study Start: February 6, 2026
• Primary Completion (Estimated): August 1, 2027
• Study Completion (Estimated): August 1, 2027
• Last Updated: September 9, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)