PROJECT SUMMARY/ABSTRACT
Eating disorders (EDs) are severe mental illnesses. Efficacy rates of evidence-based treatments
are low (<50% response) and relapse rates are high (>35% relapse after treatment). The low
treatment response and high relapse rates are due, in part, to the fact that EDs are heterogeneous
conditions. As such, idiographic (i.e., one person) models are needed that can predict and
ultimately prevent, onset of both problematic ED behaviors (e.g., purging, binge eating) and
remission/relapse. The current renewal application capitalizes on our existing data collection
(N=120 ED) to both increase our sample size (N=140) and extend data collection to two years of
follow-up. Our study goals are to: (1) characterize and predict shorter-and-longer-term relapse
and remission (2) use real-time physiological data algorithms to predict onset of ED behaviors,
relapse, and remission. We will use a multiple units of analysis approach combined with novel,
cutting-edge advances in idiographic modeling. In our currently funded proposal, we collected
intensive real-time data using mobile and sensor-technology from 120 individuals with a diagnosis
of anorexia nervosa (AN), atypical AN, and bulimia nervosa across 30 days and assessed follow-
up at 1-month and 6-months. In this renewal we will collect additional follow-ups at 18-month and
2-years and include behavioral assessments of body disturbances and behavioral avoidance. We
will also collect a new subsample of participants (n=20) and include additional assessment of
global positioning system (GPS), the sleep-wake cycle, and circadian rhythm disruption (CR).
These additional assessments will improve characterization of relapse, capture a greater
percentage of relapse events (~35% across two years), improve accuracy of prediction for ED
behaviors, relapse, and remission, and identify which features (e.g., GPS, sleep-wake cycle)
contribute to improved accuracy. Specific aims are to: (1) well-characterize longer-term (18 month
and 2 years) relapse/remission in the existing sample of EDs, (2) test if both idiographic EMA and
physiological (HR/HRV, EDA, ACC) features predict longer-term relapse/remission and (3)
determine if the addition of GPS and sleep-wake ACC & CR data improve accuracy of our
predictive algorithms. The proposed research uses highly innovative methods, combining
intensive longitudinal data collection methods, all remote procedures, novel advances in
idiographic modeling and sensor-technology, and state-of-the-art machine learning techniques.
These data will lead directly to novel therapeutics such as just-in-time mobile and sensor alert
systems that can provide guidance to both clinicians and patients on how to prevent problematic
ED behaviors and ultimately increase remission and decrease relapse rates.
