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Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress
dc.contributor.author | Cipresso, Pietro | |
dc.contributor.author | Colombo, Desirée | |
dc.contributor.author | Riva, Giuseppe | |
dc.date.accessioned | 2019-05-08T15:47:54Z | |
dc.date.available | 2019-05-08T15:47:54Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Cipresso, Pietro; Colombo, Desirée; Riva, Giuseppe. "Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress." Sensors, 2019, vol. 19, núm. 4, p.781 | ca_CA |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10234/182443 | |
dc.description.abstract | The 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.extent | 18 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | MDPI | ca_CA |
dc.relation.isPartOf | Sensors, 2019, vol. 19, núm. 4, p.781 | ca_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.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.subject | computational psychometrics | ca_CA |
dc.subject | psychophysiology | ca_CA |
dc.subject | psychological stress | ca_CA |
dc.subject | acute mental stress | ca_CA |
dc.subject | acute time-limited stressors | ca_CA |
dc.subject | Stroop color word task | ca_CA |
dc.subject | arithmetic task | ca_CA |
dc.title | Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.3390/s19040781 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.mdpi.com/1424-8220/19/4/781 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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Excepto si se señala otra cosa, la licencia del ítem se describe como: © 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/).