Large-scale Wearable Data Reveal Digital Phenotypes for Daily-life Stress Detection

Overview

Journal NPJ Digit Med

Specialty Medical Informatics

Date 2019 Jul 16

PMID 31304344

Citations 48

Authors

Elena Smets

Emmanuel Rios Velazquez

Giuseppina Schiavone

Imen Chakroun

Ellie DHondt

Walter De Raedt

Jan Cornelis

Olivier Janssens

Sofie Van Hoecke

Stephan Claes

Ilse Van Diest

Chris Van Hoof

Affiliations

Soon will be listed here.

Abstract

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.

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