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• W2904966308 abstract "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." @default.
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• W2904966308 date "2018-12-12" @default.
• W2904966308 modified "2023-10-16" @default.
• W2904966308 title "Large-scale wearable data reveal digital phenotypes for daily-life stress detection" @default.
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• W2904966308 doi "https://doi.org/10.1038/s41746-018-0074-9" @default.
• W2904966308 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6550211" @default.
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