Protocol: WorkoutCPP - a pilot series of N-of-1 trials evaluating RL-generated adaptive exercise recommendations for pelvic pain management

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This protocol describes N-of-1 trials evaluating a reinforcement learning-based mobile health intervention for personalized exercise recommendations in women with chronic pelvic pain disorders.

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This protocol describes a pilot series of N-of-1 trials evaluating an adaptive mobile health intervention that uses reinforcement learning to personalize exercise recommendations for individuals with chronic pelvic pain disorders. The study enrolls 45 participants aged 18 to 55 who undergo alternating baseline and intervention phases to assess adherence, retention, and algorithm interpretability while accounting for daily symptom variability. Although the design allows for detailed within-person assessment, the small sample size and potential for participant inference limit the generalizability of the findings, which are intended primarily to inform future confirmatory research. Relevance to endometriosis: listed as one indication for GnRH antagonists, though the paper's main focus is uterine fibroids.

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Abstract

Introduction: Chronic pelvic pain disorders (CPPDs) affect up to one in four reproductive-age women and are characterized by fluctuating, heterogeneous symptoms that complicate engagement in regular physical exercise. Standard programs rarely adjust to daily symptom variability. WorkoutCPP is a pilot series of N-of-1 trials evaluating the feasibility of an adaptive mobile-health (mHealth) intervention using reinforcement learning (RL) to personalize exercise for individuals with CPPDs. Methods and Analysis: This pilot study consists of a series of N-of-1 trials employing a randomized ABAB or BABA design, in which 45 participants (aged 18-55 years) with CPPDs each serve as their own control. Each participant completes one baseline week followed by four alternating 2-week intervention phases (A = generic exercise guidance; B = adaptive RL-generated recommendations), totaling 9 weeks. The RL agent integrated into the ehive app suggests daily exercise recommendations based on participant-reported pain and prior exercise behavior. Primary outcomes are exercise recommendation adherence rate and participant retention rate; safety and additional feasibility indicators are also monitored. Secondary outcomes assess algorithm interpretability. Exploratory outcomes assess contextual sensitivity of the RL algorithm and changes in pain and behavioral engagement. Ethics and Dissemination: Ethical approval was granted by the Icahn School of Medicine IRB (Protocol # STUDY-23-00721). Electronic informed consent will be obtained. Findings will be shared via peer-reviewed publications, conferences, and de-identified data summaries. Registration: This study protocol was publicly registered on the Open Science Framework (OSF) Registries (URL: https://osf.io/d9f36) prior to participant enrollment, to support international open-science data transparency and reproducibility standards for mobile health adaptive intervention trials. The study was subsequently registered on ClinicalTrials.gov ( NCT07810218 ), consistent with ICMJE recommendations for trials informing future confirmatory research. All documented study procedures match the baseline human subjects tracking mechanisms approved by the Icahn School of Medicine at Mount Sinai Institutional Review Board (Protocol # STUDY-23-00721).
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Abstract

Introduction Chronic pelvic pain disorders (CPPDs) affect up to one in four reproductive-age women and are characterized by fluctuating, heterogeneous symptoms that complicate engagement in regular physical exercise. Standard programs rarely adjust to daily symptom variability. WorkoutCPP is a pilot series of N-of-1 trials evaluating the feasibility of an adaptive mobile-health (mHealth) intervention using reinforcement learning (RL) to personalize exercise for individuals with CPPDs.

Methods

and Analysis This pilot study consists of a series of N-of-1 trials employing a randomized ABAB or BABA design, in which 45 participants (aged 18-55 years) with CPPDs each serve as their own control. Each participant completes one baseline week followed by four alternating 2-week intervention phases, totaling 9 weeks, comparing an active comparator arm (A = generic exercise guidance) and an experimental arm (B = adaptive RL-generated recommendations). The RL agent integrated into the ehive app suggests daily exercise recommendations based on participant-reported pain and prior exercise behavior. Primary outcomes are exercise recommendation adherence rate and participant retention rate; safety and additional feasibility indicators are also monitored. Secondary outcomes assess algorithm interpretability. Exploratory outcomes assess contextual sensitivity of the RL algorithm and changes in pain and behavioral engagement. Ethics and Dissemination Ethical approval was granted by the Icahn School of Medicine IRB (Protocol # STUDY-23-00721). Electronic informed consent will be obtained. Findings will be shared via peer-reviewed publications, conferences, and de-identified data summaries. Registration This study protocol was publicly registered on the Open Science Framework (OSF) Registries (URL: https://osf.io/d9f36) prior to participant enrollment, to support international open-science data transparency and reproducibility standards for mobile health adaptive intervention trials. The study was subsequently registered on ClinicalTrials.gov (NCT07810218), consistent with ICMJE recommendations for trials informing future confirmatory research. All documented study procedures match the baseline human subjects tracking mechanisms approved by the Icahn School of Medicine at Mount Sinai Institutional Review Board (Protocol # STUDY-23-00721). Strengths and Limitations This study represents the first application of reinforcement-learning–based adaptive exercise recommendations for individuals with chronic pelvic pain disorders. N-of-1 trial framework enables detailed within-person assessment of feasibility and individual responses. Integration of wearable and app-based data allows real-time contextual adaptation. While the ABAB/BABA design supports within-person comparisons, variability across participants (person-level differences) may limit the generalizability of findings. As a pilot study, the sample is small, findings are exploratory and intended to inform the design of future confirmatory trials. Although participants are blinded to their assigned arm at any given time, they may infer which study arm they are part of based on the nature of the recommendations. Competing Interest Statement The authors have declared no competing interest. Clinical Trial NCT07810218 Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of the Icahn School of Medicine at Mount Sinai gave ethical approval for this work (Protocol Number: IRB-23-01183). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Trial Sponsor: Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029. The sponsor had no role in study design, data collection, analysis, interpretation, or the decision to submit for publication. Funding was provided by the Hasso Plattner Institute Transatlantic Pilot Award (co-PIs: Ensari, Konigorski). Study oversight is provided by the PI (I.E.) in consultation with the co-investigator (S.K.); no independent steering committee has been established given the pilot scope and minimal risk profile of the study.

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