[New York, NY] September 30,2026—Researchers at the Icahn School of Medicine at Mount Sinai have developed an artificial intelligence (AI) approach using data from wearable devices that can forecast upcoming periods of prolonged sitting in women with chronic pelvic pain disorders.
The findings, published in the September 30 online issue of npj Women's Health [DOI: 10.1038/s44294-026-00156-5], could inform personalized digital health tools that prompt the individual to move, such as by taking a short walk, at the right time while minimizing unnecessary alerts.
Chronic pelvic pain affects an estimated one in seven women and frequently occurs in people with conditions such as endometriosis, adenomyosis, and uterine fibroids. They are often associated with prolonged sitting because of pain, fatigue, and other symptoms that impact daily life. While regular movement can help manage symptoms and improve overall health, generic advice to "sit less and move more" often fails to account for the realities of living with these conditions.
The study shows that wearable devices may do more than count steps. Using data collected over time from wearables of women with chronic pelvic pain, the researchers developed a forecasting model that can identify when prolonged sedentary periods are likely to occur during waking hours. This could allow a future digital intervention to deliver a brief reminder to stand up or take a short walk before prolonged inactivity begins.
"Our goal was to determine whether everyday wearable devices could serve as an early-warning system for prolonged sitting in women living with chronic pelvic pain," says senior author Ipek Ensari, PhD , Assistant Professor of Artificial Intelligence and Human Health at the Icahn School of Medicine and a member of the Hasso Plattner Institute of Digital Health at Mount Sinai. "Rather than offering generic advice after the fact, we wanted to determine whether we could anticipate these moments and support people with simple, well-timed prompts that fit naturally into their daily lives."
The research team analyzed wearable data from 134 women with chronic pelvic pain disorders, primarily endometriosis, along with 61 healthy participants as a comparison group. Participants wore Fitbit devices for up to 90 days, generating minute-by-minute information about physical activity, heart rate, and sleep.
Using approximately 10 days of each participant's data, the team trained personalized forecasting models to predict activity levels one hour ahead. They then tested whether those forecasts could identify 15-minute periods of sedentary behavior during waking hours, a timeframe that could allow a brief movement break, which the researchers called an "exercise snack."
The work challenged the assumption that health AI must be increasingly complex. Relatively simple, interpretable models forecasted prolonged sitting as accurately as more computationally intensive deep-learning approaches evaluated in the study.
"We were surprised by how well the simplest models performed," says lead author Jannes Jegminat, PhD, a former postdoctoral research fellow at the Icahn School of Medicine at Mount Sinai. "More complex AI is not always better. Lightweight, interpretable models can accurately forecast sedentary behavior while being practical enough to run directly on a person's own device, which also helps protect privacy."
Making future tools more feasible to run directly on a person's phone or wearable could reduce computational demands and the need to transmit sensitive data to remote servers, say the investigators.
The models also remained robust even in instances of incomplete data, which can happen when participants remove their devices or forget to synchronize them. This suggests that everyday data from wearables may support meaningful predictions under real-world conditions rather than only in controlled laboratory settings.
"This study suggests that predicting prolonged sitting is feasible, even if the individual has chronic conditions that might impact their daily routine," says Dr. Ensari. "The next step is determining whether delivering personalized movement prompts based on those predictions actually helps reduce sedentary time, improves symptoms, and enhances quality of life. Those questions will require prospective clinical trials."
The researchers believe the work could ultimately support digital health tools that feel less like constant reminders and more like a personalized coach, delivering only a small number of meaningful prompts each day while minimizing unnecessary notifications that often lead users to ignore health apps.
Beyond chronic pelvic pain, the approach may also apply to other chronic conditions in which prolonged sitting contributes to poorer health outcomes.
The research team is now working to incorporate the forecasting framework into a just-in-time adaptive intervention, which will test whether personalized, AI-guided movement prompts can reduce sedentary time and improve symptoms among women living with chronic pelvic pain disorders.
The paper is titled "Robust forecasting of sedentary bouts in chronic pelvic pain disorders for on-device learning and real-time deployment."
The authors, as listed in the journal, are Jannes Jegminat, Samia Shahnawaz, Jovita Rodrigues, Matteo Danieletto, Kyle Landell, Gabriele Campanella, Carol Ewing Garber, Zahi A. Fayad, and Ipek Ensari.
The work was funded in part by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R01HD108263, and by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences.
For more Mount Sinai artificial intelligence news, visit https://icahn.mssm.edu/about/artificial-intelligence .
About Mount Sinai's WindreichDepartment of Artificial Intelligence and Human Health
Led by Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai—an international authority on the safe, effective, and ethical use of AI in health care—Mount Sinai's Windreich Department of Artificial Intelligence and Human Health is the firstof its kind at a U.S. medical school, pioneering transformative advancements at the intersection of artificial intelligence and human health.
The Department is committed to leveraging AI in a responsible, effective, ethical, and safe manner to transform research, clinical care, education, and operations. By bringing together world-class AI expertise, cutting-edge infrastructure, and unparalleled computational power, the department is advancing breakthroughs in multi-scale, multimodal data integration while streamlining pathways for rapid testing and translation into practice.
The Department benefits from dynamic collaborations across Mount Sinai, including with the Hasso Plattner Institute for Digital Health at Mount Sinai—a partnership between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System—which complements its mission by advancing data-driven approaches to improve patient care and health outcomes.
At the heart of this innovation is the renowned Icahn School of Medicine at Mount Sinai, which serves as a central hub for learning and collaboration. This unique integration enables dynamic partnerships across institutes, academic departments, hospitals, and outpatient centers, driving progress in disease prevention, improving treatments for complex illnesses, and elevating quality of life on a global scale.
In 2024, the Department's innovative NutriScan AI application, developed by the Mount Sinai Health System Clinical Data Science team in partnership with Department faculty, earned Mount Sinai Health System the prestigious Hearst Health Prize. NutriScan is designed to facilitate faster identification and treatment of malnutrition in hospitalized patients. This machine learning tool improves malnutrition diagnosis rates and resource utilization, demonstrating the impactful application of AI in health care.
