Advanced search
1 file | 1.29 MB

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

(2018) NPJ DIGITAL MEDICINE. 1. p.1-10
Author
Organization
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.
Keywords
HEART-RATE-VARIABILITY, RECOGNITION, QUALITY, DEPRESSION, SENSORS, HEALTH, MODEL

Downloads

  • DS194.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 1.29 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Smets, Elena, Emmanuel Rios Velazquez, Giuseppina Schiavone, Imen Chakroun, Ellie D’Hondt, Walter De Raedt, Jan Cornelis, et al. 2018. “Large-scale Wearable Data Reveal Digital Phenotypes for Daily-life Stress Detection.” Npj Digital Medicine 1: 1–10.
APA
Smets, E., Velazquez, E. R., Schiavone, G., Chakroun, I., D’Hondt, E., De Raedt, W., Cornelis, J., et al. (2018). Large-scale wearable data reveal digital phenotypes for daily-life stress detection. NPJ DIGITAL MEDICINE, 1, 1–10.
Vancouver
1.
Smets E, Velazquez ER, Schiavone G, Chakroun I, D’Hondt E, De Raedt W, et al. Large-scale wearable data reveal digital phenotypes for daily-life stress detection. NPJ DIGITAL MEDICINE. London: Nature Publishing Group; 2018;1:1–10.
MLA
Smets, Elena, Emmanuel Rios Velazquez, Giuseppina Schiavone, et al. “Large-scale Wearable Data Reveal Digital Phenotypes for Daily-life Stress Detection.” NPJ DIGITAL MEDICINE 1 (2018): 1–10. Print.
@article{8587720,
  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.},
  articleno    = {67},
  author       = {Smets, Elena and Velazquez, Emmanuel Rios and Schiavone, Giuseppina and Chakroun, Imen and D'Hondt, Ellie and De Raedt, Walter and Cornelis, Jan and Janssens, Olivier and Van Hoecke, Sofie and Claes, Stephan and Van Diest, Ilse and Van Hoof, Chris},
  issn         = {2398-6352},
  journal      = {NPJ DIGITAL MEDICINE},
  keyword      = {HEART-RATE-VARIABILITY,RECOGNITION,QUALITY,DEPRESSION,SENSORS,HEALTH,MODEL},
  language     = {eng},
  pages        = {67:1--67:10},
  publisher    = {Nature Publishing Group},
  title        = {Large-scale wearable data reveal digital phenotypes for daily-life stress detection},
  url          = {http://dx.doi.org/10.1038/s41746-018-0074-9},
  volume       = {1},
  year         = {2018},
}

Altmetric
View in Altmetric
Web of Science
Times cited: