Background Flares are an inherent part of the rheumatoid arthritis (RA) disease course and may impact clinical patient outcomes. The ability to predict flares between clinic visits based on real-time longitudinal patient-generated data could potentially allow for timely interventions avoid worsening. For intensively-collected data, machine learning methods offer benefits over traditional statis...

Intensive longitudinal data is increasingly used to study state-like processes such as changes in daily stress. Measures aimed at collecting require the same level of scrutiny regarding scale reliability traditional questionnaires. The most prevalent methods assess intensive measures are based on generalizability theory or a multilevel factor analytic approach. However, application recent impro...