Abstract
Depression is highly recurrent and heterogenous in its individual course, requiring a personalized treatment approach. Patients today can collect large volumes of personal data via smartphones and smartwatches and may utilize them for their treatment and self-management. We aim to provide proof-of-concept that these data can (i) serve as an objective marker of and (ii) predict the daily and weekly self-reported depression severity within individuals with depressive disorders. In this exploratory study, 15 adult patients with depressive disorders will collect self-report and biosensor data over the course of one year. Participants will (a) attend three in-person appointments (at baseline, 6 months, and 12 months), (b) self-report daily and weekly depressive symptoms, (c) continuously collect sensor data via the "iTrackDepression" app on their Android smartphone (app usage, phone calls, phonetic parameters from voice recordings), and (d) wear a Samsung Galaxy Watch 5® to record data from the accelerometer, step sensor, light sensor, and heart rate sensor. We will apply multilevel correlations, vector-autoregressive models, and Machine Learning approaches to identify individual patterns in the data, particularly in the relationships between biosensor data and self-reported depressive symptoms. Enhancing the understanding of individual disease trajectories through data from smartphones and smartwatches could allow for classical, digital, and self-management interventions for depression to be delivered in a manner and at a time specifically tailored to the individual's needs. Clinical trial registration number: DRKS00032618 (https://drks.de/search/en/trial/DRKS00032618).