Large-scale wearable data reveal digital phenotypes for daily-life stress detection

• Published in: NPJ digital medicine Vol. 1; no. 1; p. 67
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12-12-2018; Nature Publishing Group
• Subjects: 692/53/2423; 692/700/459/1748; Biomedicine; Biotechnology; Digital technology; Health informatics; Medicine; Medicine & Public Health; Physiology; Precision medicine; Wearable computers; Predictive markers; Preventive medicine
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-018-0074-9

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.