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.