Digital phenotyping

The pipeline released here for the deep phenotyping of sleep, as the “DPSleep” software package, uses (a) a stepwise algorithm to detect missing data; (b) within-individual, minute-based, spectral power percentiles of activity; and (c) iterative, forward- and backward-sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of derived sleep features and correction for time zone changes. In this report, we illustrate the pipeline with data from participants studied for more than 200 days each. Actigraphy-based measures of sleep duration are associated with self-report rating of sleep quality. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data. We discuss the uses of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (e.g., smartphone, tablet) to measure individual differences across a wide range of behavioral variation in health and disease.