Large-scale wearable data reveal digital phenotypes for daily-life stress detection
Open accessAbstract
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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Wearable activity trackers are increasingly widely used.
“Wearable activity trackers are increasingly widely used and provide an opportunity to harness sensor data for detection of different health conditions [7].”
From Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling (2021) · cites this paper as [7]
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References 63 · Cited by 250
Research works printed but not linked: 1
- [13]Sun, F.-T. et al. Activity-aware mental stress detection using physiological sensors. in Proc. International Conference on Mobile Computing, Applications, and Services (MobiCASE), Vol. 76, 1–20 (2010).
- Authors
- Smets, E., Rios Velazquez, E., Schiavone, G., Chakroun, I., D’Hondt, E., De Raedt, W., Cornelis, J., Janssens, O., Van Hoecke, S., Claes, S., Van Diest, I., & Van Hoof, C.
- Affiliations
- IMEC (BE)
- Imec the Netherlands (NL)
- Ghent University (BE)
- KU Leuven (BE)
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2018-12-12
- Volume
- 1
- Issue
- 1
- Pages
- 67-67
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6550211
- OpenAlex
- W2904966308
- Cited by
- 250
- References
- 63
- Citations by year
- 2026: 39 · 2025: 37 · 2024: 27 · 2023: 26 · 2022: 23 · 2021: 68 · 2020: 23 · 2019: 7
- FWCI
- 32.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Agentschap Innoveren en Ondernemen
- Vlaamse regering
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- daily stress, digital phenotypes, wearable sensors, physiological signals, depression, anxiety, self-reported health, smartphone data, multi-sensor data, precision medicine
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