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Title & Speakers	Event
Predicting women's chronic disease flares from wearables 2025-12-12 · 19:00 Ipek Ensari – Assistant Professor @ Icahn School of Medicine at Mount Sinai Heart rate variability (HRV) is a well-known digital biomarker and is increasingly available in consumer wearables. However, extracting actionable predictions from HRV data, in particular for clinical use, remains challenging. Using specialized R packages, this presentation demonstrates how to model 24-hour periodic patterns in HRV metrics as non-linear circadian components to predict chronic disease flares. Grounded in real-life data from a NIH-funded longitudinal mHealth-based study of female chronic pelvic pain disorders, we will investigate how mixed-effects cosinor regression accommodates individual variation and complex interactions between circadian parameters and time-varying covariates (menstrual cycle, physical activity, sleep quality). These examples aim to illustrate how patient-generated data from everyday wearables can democratize access to predictive medicine by helping patient-users maximize the benefits of their data to gain predictive insights into their health status. hrv wearables r cosinor regression mixed-effects cosinor regression digital phenotyping mhealth	The Data-Powered Patient: Predicting Women's Chronic Disease from Wearables

Heart rate variability (HRV) is a well-known digital biomarker and is increasingly available in consumer wearables. However, extracting actionable predictions from HRV data, in particular for clinical use, remains challenging. Using specialized R packages, this presentation demonstrates how to model 24-hour periodic patterns in HRV metrics as non-linear circadian components to predict chronic disease flares. Grounded in real-life data from a NIH-funded longitudinal mHealth-based study of female chronic pelvic pain disorders, we will investigate how mixed-effects cosinor regression accommodates individual variation and complex interactions between circadian parameters and time-varying covariates (menstrual cycle, physical activity, sleep quality). These examples aim to illustrate how patient-generated data from everyday wearables can democratize access to predictive medicine by helping patient-users maximize the benefits of their data to gain predictive insights into their health status.

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