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Using wearable biosensors and ecological momentary assessments for the detection of prolonged stress in real life

View ORCID ProfileRayyan Tutunji, Nikos Kogias, Bob Kapteijns, Martin Krentz, Florian Krause, Eliana Vassena, Erno Hermans
doi: https://doi.org/10.1101/2021.06.29.450360
Rayyan Tutunji
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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  • ORCID record for Rayyan Tutunji
  • For correspondence: rayyan.tutunji@donders.ru.nl
Nikos Kogias
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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Bob Kapteijns
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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Martin Krentz
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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Florian Krause
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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Eliana Vassena
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
2Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
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Erno Hermans
1Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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Abstract

Emerging efforts toward prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. We used ecological momentary assessments (EMA) combined with wearable biosensors to investigate whether these can be used to detect periods of prolonged stress. During stressful high-stake exam (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal was decreased on average during the exam week. Time-resolved analyses revealed peaks in physiological arousal associated with both self-reported stress and self-reported positive affect, while the overall decrease in physiological arousal was mediated by lower positive affect during the stress period. We then used machine learning to show that a combination of EMA and physiology yields optimal classification of week types. Our findings highlight the potential of wearable biosensors in stress-related mental-health monitoring, but critically show that psychological context is essential for interpreting physiological arousal detected using these devices.

Teaser Smartwatches combined with daily diaries of mood can detect stress periods using individualized machine learning models.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Only cosmetic changes were performed in this revision. Minor textual corrections were applied. Figures were reformatted, and Figure 5 has been changed for easier interpretation.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 14, 2021.
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Using wearable biosensors and ecological momentary assessments for the detection of prolonged stress in real life
Rayyan Tutunji, Nikos Kogias, Bob Kapteijns, Martin Krentz, Florian Krause, Eliana Vassena, Erno Hermans
bioRxiv 2021.06.29.450360; doi: https://doi.org/10.1101/2021.06.29.450360
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Using wearable biosensors and ecological momentary assessments for the detection of prolonged stress in real life
Rayyan Tutunji, Nikos Kogias, Bob Kapteijns, Martin Krentz, Florian Krause, Eliana Vassena, Erno Hermans
bioRxiv 2021.06.29.450360; doi: https://doi.org/10.1101/2021.06.29.450360

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