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Wearable AI forecasts prolonged sitting in women with chronic pelvic pain (opens in a new tab)

medicalxpress.com · 2026-09-30

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Robust forecasting of sedentary bouts in chronic pelvic pain disorders for on-device learning and real-time deployment

Npj Women's Health · 2026
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Claim 1 of 6Not checkedResearchers at the Icahn School of Medicine at Mount Sinai developed an AI approach that uses wearable-device data to forecast upcoming periods of prolonged sitting in women with chronic pelvic pain disorders.View evidenceHide evidence

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Claim 2 of 6Not checkedThe findings were published in the Sept. 30 online issue of npj Women's Health and could inform personalized digital health tools that prompt an individual to move at the right time while minimizing unnecessary alerts.View evidenceHide evidence

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Claim 3 of 6Not checkedThe study analyzed wearable data from 134 women with chronic pelvic pain disorders, primarily endometriosis, plus 61 healthy comparison participants, who wore Fitbit devices for up to 90 days.View evidenceHide evidence

As stated134 women with chronic pelvic pain disorders and 61 healthy participants; up to 90 days

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Claim 4 of 6Not checkedUsing about 10 days of each participant's data, the researchers trained personalized forecasting models to predict activity levels one hour ahead and then tested whether those forecasts could identify 15-minute periods of sedentary behavior during waking hours.View evidenceHide evidence

As statedone hour ahead; 15-minute sedentary periods; about 10 days of data

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Claim 5 of 6Not checkedRelatively simple, interpretable models forecast prolonged sitting as accurately as more computationally intensive deep-learning approaches evaluated in the study, and the models remained robust when data were incomplete.View evidenceHide evidence

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Claim 6 of 6Not checkedThe researchers say the next step is to test whether personalized movement prompts based on the forecasts reduce sedentary time, improve symptoms, and enhance quality of life in prospective clinical trials.View evidenceHide evidence

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Robust forecasting of sedentary bouts in chronic pelvic pain disorders for on-device learning and real-time deployment

npj Women's Health · 2026

Source record (opens in a new tab)

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Papers considered

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Crossref, PubMed, Europe PMC · 16 candidate papers

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Robust forecasting of sedentary bouts in chronic pelvic pain disorders for on-device learning and real-time deployment

Npj Women's Health · 2026 · Crossref

Source (opens in a new tab)
Candidate

Effects of Replacing Prolonged Sedentary Bouts with Short Sedentary Bouts or Physical Activity On Mortality

Medicine & Science in Sports & Exercise · 2018 · Crossref

Source (opens in a new tab)
Candidate

Table 5: Correlations between weight change, weight gain and weight loss and baseline sedentary time (1-min bouts) by site and gender.

Crossref

Source (opens in a new tab)

And 10 more candidates considered.