Wearable AI forecasts prolonged sitting in women with chronic pelvic pain
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Researchers at the Icahn School of Medicine at Mount Sinai have developed an artificial intelligence (AI) approach that uses data from wearable devices to forecast upcoming periods of prolonged sitting in women with chronic pelvic pain disorders.
The findings, published in the Sept. 30 online issue of npj Women's Health, could inform personalized digital health tools that prompt an individual to move, such as by taking a short walk, at the right time while minimizing unnecessary alerts.
Chronic pelvic pain affects an estimated 1 in 7 women and frequently occurs in people with conditions such as endometriosis, adenomyosis and uterine fibroids. These conditions are often associated with prolonged sitting because of pain, fatigue and other symptoms that affect 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.
Forecasting a window for movement
The study shows that wearable devices may do more than count steps. Using data collected over time from wearables worn by 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, Ph.D., 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."
Ninety days of wearable data
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 time frame that could allow a brief movement break, which the researchers called an "exercise snack."
Simpler models held their own
The work challenged the assumption that health AI must be increasingly complex. Relatively simple, interpretable models forecast 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, Ph.D., 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 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, the investigators say.
The models also remained robust when data were incomplete, 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.
Movement prompts face clinical tests
"This study suggests that predicting prolonged sitting is feasible, even if the individual has chronic conditions that might impact their daily routine," says 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.
More information
Robust forecasting of sedentary bouts in chronic pelvic pain disorders for on-device learning and real-time deployment, npj Women's Health (2026). DOI: 10.1038/s44294-026-00156-5
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Women's healthObstetrics & gynecologyFitness & Physical activity Provided by The Mount Sinai Hospital Who's behind this story?
Swati Mestri
Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile →
Robert Egan
Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile →
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