Large-scale wearable data reveal digital phenotypes for daily-life stress detection - Smets, Elena; Rios Velazquez, Emmanuel; Schiavone, Giuseppina; Chakroun, Imen; D’Hondt, Ellie; De Raedt, Walter; Cornelis, Jan; Janssens, Olivier; Van Hoecke, Sofie; Claes, Stephan; Van Diest, Ilse; Van Hoof, Chris (NPJ digital medicine, 2018)
Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects’ demographics, baseline psychological information, and five consecutive days of free-living physiological and contextual measurements, collected through wearable devices and smartphones. This dataset represents a healthy population, showing associations between wearable physiological signals and self-reported daily-life stress. Using a data-driven approach, we identified digital phenotypes characterized by self-reported poor health indicators and high depression, anxiety and stress scores that are associated with blunted physiological responses to stress. These results emphasize the need for large-scale collections of multi-sensor data, to build personalized stress models for precision medicine.