JMIR Publications

ABSTRACT

Background:

Data that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge-eating disorder (BED) and bulimia nervosa (BN), for which we lack highly efficacious, enduring, and accessible treatments. However, in contrast to the ease of digital data collection, these highly complex data present unique methodological challenges that invite innovative solutions.

Objective:

We describe the digital phenotyping component of the Binge Eating Genetics Initiative (BEGIN; [1]), which uses personal digital device data to capture dynamic patterns of risk for binge/purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathology and to reduce risk for eating disorder behaviors through just-in-time interventions. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application.

Methods:

All study procedures were approved by the University of North Carolina Biomedical Institutional Review Board. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches across 30 days. Data were inspected and cleaned to account for user and device recording errors including duplicate entries and unreliable heart rate and steps values. Across participants, the proportion of data points removed during cleaning ranged from < 0.1% to 2.4%, depending on the data source. To prepare data for multivariate time series analysis, we used a novel data handling approach to address the variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feelings ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge/purge episodes.

Results:

Data collection and cleaning are complete. Between August 2017 and May 2021, 1,019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8,274 binge or purge events, and 85,200 feelings observations. Analysis will begin in Spring 2022.

Conclusions:

We provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. Results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data. Clinical Trial: The ClinicalTrials.gov identifier is NCT04162574. November 14, 2019, Retrospectively Registered.