TITLE: “Detection of Symptoms of Depression Using Data From the iPhone and Apple Watch.”
ABSTRACT: Digital health data from consumer wearable devices and smartphones have the potential to improve our understanding of mental illness. However, in conditions like depression, there is not yet a consistent uniform measurement tool whose result can be reliably used as a gold standard measure of depression severity. This work seeks to specify what symptoms and dimensions of depression can be detected using vitals, activity, and sleep monitored by consumer wearable devices. Machine learning models are fit to digital health data and used to detect responses to individual questions from self-reports as well as summary scores. Data is analyzed from an ongoing study with data from the Apple Watch, iPhone, and validated self-reports. The digital health data investigated was found to detect depression severity and specific symptoms like poor appetite, aspects of anhedonia, and sleep timings (ROC AUC 0.63 to 0.72).