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Personalized Exercise Recommendations for Chronic Pelvic Pain Using Reinforcement Learning (WorkoutCPP)

3. September 2026 aktualisiert von: Ipek Ensari, Icahn School of Medicine at Mount Sinai

WorkoutCPP: A Pilot Series of N-of-1 Trials Evaluating RL-Generated Adaptive Exercise Recommendations for Pelvic Pain Management

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.

Studienübersicht

Status

Rekrutierung

Bedingungen

Intervention / Behandlung

Detaillierte Beschreibung

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.

Studientyp

Interventionell

Einschreibung (Geschätzt)

45

Phase

  • Unzutreffend

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienkontakt

Studieren Sie die Kontaktsicherung

Studienorte

    • New York
      • New York, New York, Vereinigte Staaten, 10029
        • Rekrutierung
        • Icahn School of Medicine at Mount Sinai
        • Kontakt:
        • Kontakt:
        • Hauptermittler:
          • Ipek Ensari

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

  • Erwachsene

Akzeptiert gesunde Freiwillige

Nein

Beschreibung

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).

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

  • Hauptzweck: Sonstiges
  • Zuteilung: Zufällig
  • Interventionsmodell: Crossover-Aufgabe
  • Maskierung: Single

Waffen und Interventionen

Teilnehmergruppe / Arm
Intervention / Behandlung
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.
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.
Aktiver Komparator: 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.
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.

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Exercise Recommendation Adherence Rate
Zeitfenster: At 9 weeks at study completion
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
Zeitfenster: At 9 weeks at study completion
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

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Reinforcement Learning Agent Action Entropy Over Time
Zeitfenster: At 9 weeks at study completion
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.
At 9 weeks at study completion

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Sponsor

Mitarbeiter

Ermittler

  • Hauptermittler: Ipek Ensari, PhD, Icahn School of Medicine at Mount Sinai
  • Hauptermittler: Stefan Konigorski, PhD, Department of Computational Precision Nutrition, German Institute of Human Nutrition

Publikationen und hilfreiche Links

Die Bereitstellung dieser Publikationen erfolgt freiwillig durch die für die Eingabe von Informationen über die Studie verantwortliche Person. Diese können sich auf alles beziehen, was mit dem Studium zu tun hat.

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Tatsächlich)

6. Februar 2026

Primärer Abschluss (Geschätzt)

1. August 2027

Studienabschluss (Geschätzt)

1. August 2027

Studienanmeldedaten

Zuerst eingereicht

3. September 2026

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

3. September 2026

Zuerst gepostet (Tatsächlich)

9. September 2026

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

9. September 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

3. September 2026

Zuletzt verifiziert

1. September 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Andere Studien-ID-Nummern

  • STUDY-23-00721

Plan für individuelle Teilnehmerdaten (IPD)

Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?

NEIN

Beschreibung des IPD-Plans

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.

Arzneimittel- und Geräteinformationen, Studienunterlagen

Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Nein

Produkt, das in den USA hergestellt und aus den USA exportiert wird

Nein

Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .