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dc.contributor.authorCipresso, Pietro
dc.contributor.authorColombo, Desirée
dc.contributor.authorRiva, Giuseppe
dc.date.accessioned2019-05-08T15:47:54Z
dc.date.available2019-05-08T15:47:54Z
dc.date.issued2019
dc.identifier.citationCipresso, Pietro; Colombo, Desirée; Riva, Giuseppe. "Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress." Sensors, 2019, vol. 19, núm. 4, p.781ca_CA
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10234/182443
dc.description.abstractThe goal of this study was to provide reliable quantitative analyses of psycho-physiological measures during acute mental stress. Acute, time-limited stressors are used extensively as experimental stimuli in psychophysiological research. In particular, the Stroop Color Word Task and the Arithmetical Task have been widely used in several settings as effective mental stressors. We collected psychophysiological data on blood volume pulse, thoracic respiration, and skin conductance from 60 participants at rest and during stressful situations. Subsequently, we used statistical univariate tests and multivariate computational approaches to conduct comprehensive studies on the discriminative properties of each condition in relation to psychophysiological correlates. The results showed evidence of a greater discrimination capability of the Arithmetical Task compared to the Stroop test. The best predictors were the short time Heart Rate Variability (HRV) indices, in particular, the Respiratory Sinus Arrhythmia index, which in turn could be predicted by other HRV and respiratory indices in a hierarchical, multi-level regression analysis. Thus, computational psychometrics analyses proved to be an effective tool for studying such complex variables. They could represent the first step in developing complex platforms for the automatic detection of mental stress, which could improve the treatment.ca_CA
dc.format.extent18 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherMDPIca_CA
dc.relation.isPartOfSensors, 2019, vol. 19, núm. 4, p.781ca_CA
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).ca_CA
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcomputational psychometricsca_CA
dc.subjectpsychophysiologyca_CA
dc.subjectpsychological stressca_CA
dc.subjectacute mental stressca_CA
dc.subjectacute time-limited stressorsca_CA
dc.subjectStroop color word taskca_CA
dc.subjectarithmetic taskca_CA
dc.titleComputational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stressca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.3390/s19040781
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://www.mdpi.com/1424-8220/19/4/781ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).