Personalized Exercise Recommendations for Chronic Pelvic Pain Using Reinforcement Learning

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.